Engineering and environmental assessment of soilbag-based slope stabilisation for sustainable landslide mitigation in mountainous area

Changes in land use significantly impact landslide occurrence, particularly in mountainous areas in northern Thailand, where human activities such as urbanization, deforestation, and slope modifications alter natural slope angles, increasing susceptibility to landslides. To address this issue, an appropriate method using soilbags has been widely used for slope stabilisation in northern Thailand, but their effectiveness and sustainability require assessment. This research highlights the need to evaluate the stability of the soilbag-based method. In this study, a case study was conducted in northern Thailand, focusing on an area characterised by high-risk landslide potential. This research focuses on numerical evaluation the slope stability of soilbag-reinforced structures and discusses environmental sustainability. The study includes site investigations using an unmanned aerial photogrammetric survey for slope geometry evaluation and employing the microtremor survey technique for subsurface investigation. Soil and soilbag material parameters are obtained from existing literatures. Modelling incorporates hydrological data, slope geometry, subsurface conditions, and material parameters. Afterwards, the pore-water pressure results and safety factors are analysed. Finally, the sustainability of soilbags is discussed based on the Sustainable Development Goals (SDGs). The results demonstrate that soilbags effectively mitigate pore-water pressures, improve stability, and align with several SDGs objectives. This study enhances understanding of soilbags in slope stabilisation and introduces a sustainable landslide mitigation approach for landslide-prone regions.

InSAR-based landslide hazard mapping and identification of at-risk infrastructures in a selected district of North Shewa Zone, Ethiopia

The devastating nature of landslides demands a thorough understanding of their spatial distribution and the risks they pose to human settlements and infrastructural assets. In this study, we employed a combination of Interferometric Synthetic Aperture Radar (InSAR) and Geographic Information System (GIS) techniques to explore the western escarpment of the Main Ethiopian Rift, with a focus on selected districts within the northern Shewa Zone, Ethiopia. By analyzing the SAR data, we derived 28 displacement maps and utilized them to create a comprehensive landslide hazard zonation map. The results indicated significant ground displacement, particularly along the rift margins and areas characterized by rugged terrain. The hazard zones were classified based on their level of risk, with 44% classified as very low, 24% as low, 5% as moderate, 13% as high, and 14% as very high hazard zones. The accuracy of our results was evaluated using receiver operating characteristic (ROC) analysis, which was conducted utilizing landslide inventory data. The analysis demonstrated a remarkable area under the curve (AUC) value of 0.848, providing strong evidence for the validity of our findings. Additionally, our study involved a spatial and statistical assessment of major infrastructure, revealing that 20 to 28% of these properties were in hazard zones ranging from moderate to very high levels, which calls for efficient risk-reduction actions. Therefore, this finding enables stakeholders to identify high-risk areas, prioritize mitigation efforts, and minimize the impact of landslide disasters.

Novel evaluation method based on critical arch height as instability criterion for sustaining arch locked-segment-type slopes

In sustaining arch locked-segment-type slopes, natural soil arches play a key anti-sliding role in the slope's evolution. In this study, a self-developed model test device was used to simulate the whole process of deformation evolution of sustaining arch locked-segment-type slopes, and the formation of natural sustaining arch and its locking control effect on slope stability were studied. The test results show that the continuous formation and progressive destruction of the sustaining arch were observed. The sustaining arch formed in the second time has the best locking effect, and the anti-sliding force reaches its stress peak point. However, the slope is not in a critically unstable state, instead, the stress is continuously adjusted to form a larger range of soil arch to resist the slope thrust. Consequently, the slope destabilizes until the ultimate shear strength of arch foots is exceeded, at which point the critical arch height of the arch is reached. The critical arch height mechanical model for slope stability analysis was developed based on the soil arching effect and limit equilibrium theory. The applicability of the model was demonstrated by the physical test and Xintan slope data, which can provide some guidance for early warning of landslides.

Landslide assessment through integrated geoelectrical and seismic methods: A case study in Thungsong site, southern Thailand

Many landslides can cause significant damage to infrastructure, property, and human life. To study landslide structure and processes, geophysical techniques are most productive when employed in combination with other survey and monitoring tools, such as intrusive sampling. Here, the integration of electrical resistivity tomography (ERT) and seismic refraction tomography (SRT) methods is used to assess landslides in Thungsong district, Nakhon Si Thammarat, the south of Thailand, where is a hilly and seasons of prolonged rainfall region. The 2D cross-plot analysis of P-wave velocity and resistivity values obtained by these two methods is introduced to identify potential landslide-prone zones in this region. The results of the 2D cross-plot model reveal detailed image of the subsurface conditions, highlighting areas of low P-wave velocity (lower than 600 m/s) and low resistivity (lower than 600 Ωm). These areas are indicative of weak zone and are potential to be sliding materials. Moreover, an intrusive sampling data from boreholes is also used for the calibration and validation geophysical data with geological data. This can improve the accuracy of landslide assessment and develop effective mitigation strategies to reduce the risk of landslides in this area. In addition of the 2D cross-plot, the volume of sliding material is also determined from the difference of the surface and slipping plane elevations. The volume calculation of sliding material is roughly 33447.76 m3. This approach provides a preliminary tool for landslide studies and monitoring landslides in this region, thus enabling an improved understanding of slope failure processes in this context, and the basis of a landslide mitigation strategy in the future.

Modeling windthrow effects on water runoff and hillslope stability in a mountain catchment affected by the VAIA storm

Windthrows seriously affect forest landscapes, causing several issues in hydrological and geomorphological terms. In this regard, Airborne Laser Scanning (ALS) topographic data recently increased the opportunity to investigate in detail physical processes at the catchment scale. Moreover, topographically based hydrological and geomorphological models allow quantifying runoff alteration due to windthrows-driven land cover changes and detect the occurrence of land degradative processes at the sub-catchment scale. In this connection, accurate investigations about windthrows role in varying local runoff regimes over time are still obscure, as well as the possibility of predicting terrain instabilities due to windstorm occurrence. This research aims to investigate the interaction between windthrows, runoff alterations and hillslope failures affecting a landslide-prone mountain catchment (northern Italy). Hydrological HEC-HMS and geomorphological RESS models were applied. Windthrows' role in altering runoff regimes and hillslope stability was investigated starting from the elaboration of ALS-derived points clouds acquired before and after the occurrence of the Vaia storm. Digital Terrain Models (DTMs) were elaborated for the two scenarios to compare daily runoff variations and predict the activation of terrain instabilities by looking at land cover changes driven by the blowdown event at the sub-catchment detail. Results attested the key role of windstorms in altering local runoff, with a maximum relative runoff increment equal to 2.56 % and a maximum runoff difference equal to 3.12 mmh-1, as well as in encouraging the activation of the observed shallow landslide. The correlation between windthrows occurrence and runoff alterations was validated by performing regression analysis (R2 = 0.76), while the accuracy of instabilities predictions was tested through the Distance to Perfect Classification (D2PC) index and True Skill Statistic (TSS) score, respectively resulted equal to 0.076 and 0.898. This research represents a valid tool for investigating similar issues at a wider scale, also providing suggestions for promoting interventions in wind-disturbed forest areas.

Geological challenges and stabilization strategies for phyllite rock slopes: a case study of Guang-Gansu expressway in Western China

The increased occurrence and severity of natural disasters, such as landslides, have impacted the stability of phyllite rock slopes in the complex geological regions of Western China. This situation presents significant challenges for infrastructure development in the area. This study investigates the upper span bridgehead slope of Guang-Gansu expressway K550 + 031 as a case study to analyze the sliding failure mechanism of thousand rock slopes in the seismic fault zone and the supporting structure failure through field investigation and exploration. The analysis shows that the slope's rock mass is extensively fractured, primarily influenced by the Qingchuan fault zone. This geological activity leads to slope instability, worsened by seasonal rainfall. The phyllite undergoes alternating dry and wet cycles, weakening its mechanical strength, forming cracks, and accelerating slope displacement, subsidence, and cracking. This results in front slope instability, followed by gradual backward and step-by-step traction sliding deformation on both sides. The geological structure and seasonal rainfall damage the original bolt-grid beam-supporting structure. To address this issue, an anti-slide pile combined with a grid beam treatment method is proposed, and its effectiveness is verified through deep displacement monitoring. This study emphasizes the significance of integrating geological structure and seasonal rainfall impacts into infrastructure design within complex geological areas, ensuring slope and supporting structure stability.

Risk assessment of chemical release accident triggered by landslide using Bayesian network

This study evaluated the risk of 461,260,800 scenarios of chemical release accidents triggered by landslides. Several industrial accidents triggered by landslides have recently occurred in Japan; however, only a few studies have analyzed the impact of landslide-triggered chemical release accidents on the surrounding areas. Bayesian networks (BNs) have recently been used in the risk assessment of natural hazardtriggered technological accidents (Natech) to quantify uncertainties and develop methods applicable to multiple scenarios. However, the scope of BN-based quantitative risk assessment is limited to the risk assessment of explosions triggered by earthquakes and lightning. We aimed to extend the BN-based risk analysis methodology and evaluate the risk and the effectiveness of the countermeasures for specific facility. A methodology was developed to assess human health risk in the surrounding areas when n-hexane was released and dispersed into the atmosphere due to a landslide. Risk assessment results showed that the societal risk (representing the relationship between frequency and number of people suffering from a particular harm) of the storage tank closest to the slope exceeded the Netherlands' criteria, which are the safest among the criteria in the United Kingdom, Hong Kong, Denmark, and the Netherlands. Limiting the storage rate reduced the probability of one or more fatalities by up to about 40% compared with the no countermeasure case and was a more effective countermeasure than using oil fences and absorbents. Diagnostic analyses quantitatively showed that the distance between the tank and slope was the main contributing factor. The catch basin parameter contributed to the reduction in the variance of the results compared to the storage rate. This finding indicated that physical measures, such as strengthening or deepening the catch basin, are essential for risk reduction. Our methods can be applied to other natural disasters for multiple scenarios by combining it with other models.

Acquisition of high-resolution topographic information in forest environments using integrated UAV-LiDAR system: System development and field demonstration

High-resolution topographic information of landslide-prone areas plays an important role in accurate prediction and characterization of potential landslides and mitigation of landslides-associated hazards. This study presents an advanced geomorphological surveying system that integrates the light detection and ranging (LiDAR) with an unmanned aerial vehicle (UAV), a multi-rotor aerial vehicle in specific, for prediction, monitoring and forensic analysis of landslides, and for maintenance of debris-flow barriers. The test-flight over a vegetated area demonstrates that the integrated UAV-LiDAR system can provide high-resolution, three-dimensional (3D) LiDAR point clouds below canopy and vegetation in forest environments, overcoming the limitation of aerial photogrammetry and terrestrial LiDAR platforms. An algorithm is suggested to delineate the topographic information from the acquired 3D LiDAR point clouds, and the accuracy and performance of the developed UAV-LiDAR system are examined through field demonstration. Finally, two field demonstrations are presented: the forensic analysis of the recent Gokseong landslide event, and the sediment deposition monitoring for debris-flow barrier maintenance in South Korea. The developed surveying system is expected to contribute to geomorphological field surveys in vegetated, forest environments, particularly in a site that is not easily accessible.

Climate change amplified the 2009 extreme landslide event in Austria

Landslides are an important natural hazard in mountainous regions. Given the triggering and preconditioning by meteorological conditions, it is known that landslide risk may change in a warming climate, but whether climate change has already affected individual landslide events is still an open question, partly owing to landslide data limitations and methodological challenges in climate impact attribution. Here, we demonstrate the substantial influence of anthropogenic climate change on a severe event in the southeastern Alpine forelands with some estimated 952 individual landslides in June 2009. Our study is based on conditional event attribution complemented by an assessment of changes in atmospheric circulation. Using this approach, we simulate the meteorological event under observed and a range of counterfactual conditions of no climate change and explicitly predict the landslide occurrence probability for these conditions. We find that up to 10%, i.e., 95 landslides, can be attributed to climate change.

An integrated geotechnical and geophysical investigation of landslide in Chira town, Ethiopia

Landslides pose a significant threat to infrastructure, property, and human lives in many regions worldwide, including Chira town in Ethiopia. This study presents an integrated geotechnical and geophysical investigation aimed at identifying the contributing factors to landslides in Chira town, Ethiopia, with a focus on a recent landslide event. The methodology employed a combination of geotechnical and geophysical techniques to comprehensively analyze the landslide problem. The geotechnical investigation involved a detailed analysis of the soil characteristics in the area, including the composition of fine-grained soil and the determination of cohesion and angle of internal friction through triaxial testing. The geophysical investigation utilized electrical resistivity tomography to assess the subsurface soil profile. The findings revealed the presence of a massive basaltic tertiary volcanic rock layer underlying a very low resistivity layer of sticky clay soil. Through this study, it was established that rainfall, soil type, land use, elevation, and proximity to streams, slopes, and aspects were the main factors contributing to the landslide, accounting for 22.03%, 18.89%, 15.75%, 15.46%, 10.87%, 9.7%, and 7.5% of the overall influence, respectively. Based on these findings, the study proposes a range of interventions to enhance resilience against landslides, including surface drainage, the implementation of appropriate land use management practices, and the introduction of vetiver vegetation. The integration of geotechnical and geophysical methodologies provided a comprehensive understanding of the landslide problem in Chira town. The proposed interventions aim to inform future land use planning, infrastructure development, and disaster risk reduction efforts in the region. By expanding our knowledge of the mechanisms driving landslides, this study offers valuable insights that can be utilized in similar regions facing comparable geotechnical and geophysical conditions.

A review on landslide susceptibility mapping research in Bangladesh

Landslide susceptibility mapping is a common practice for landslide susceptibility assessment across the world. Like many other mountainous areas of the world, Bangladesh is facing frequent catastrophic landslides causing severe damage to the economy and society. As a result, several types of research have been conducted on landslides in Bangladesh. In the current research, a systematic review is conducted on the existing literature related to landslide susceptibility mapping to assess its contemporary trend with global research. The publications analyzed in this research were extracted from a website comprising landslide research of Bangladesh and by manual search. The aspects of the literature considered are year of publication, the journal where published, location/size of the study area, landslide inventory data type, susceptibility assessment/mapping method, thematic variables used, DEM characteristics, accuracy assessment methods and acquired accuracy of the models. The Chi-square test was conducted and correlation was measured to assess relation between selected features and map accuracy but no significant relationship was found. The studies are concentrated into three administrative districts of Chattogram, Rangamati and Cox's Bazar mainly covering the city centre. The publication rate is increasing but not following the global trend. Though various types of models are used and compared, the application of machine and deep learning algorithms are very limited and no evidence of Physically-based methods is found. Most of the cases, landslide inventory is prepared by conducting field survey, but the size is small. The research will help future practitioner in landslide susceptibility mapping research in the area.

Geophysical and numerical stability analysis of landslide incident

Landslide is known for its precarious impact on environment, resources and human life. Recently, landslide has occurred in Lalisa village, Jimma Zone, Ethiopia which harshly caused damage to life and property. The incident resulted in perilous damage of about 27 ha of accessible land. This study hence particularly aimed at investigating the root cause of the incident and analyzing safety of the sliding slope so that the applicable remedial actions can be proposed. Geophysical analysis without soil structure disturbance was adopted to investigate the vertical soil profile, morphological stratification, location and alignment of discontinuity planes. Stability analysis by using Limit Equilibrium method was carried out for both normal and worst conditions to rate safety of the failing slope. Lithology of the site is characterized by highly weathered and fractured rock units exhibiting a significant variability over a little horizontal distance and depth. The stratigraphy also constitutes loose soil near the surface and saturated layer ranging from depth of 10 m to 25 m. The slope failure occurred at the site is of deep by its type that origin of its slip plane extends up to a depth of 12 m from the surface. Furthermore, factor of safety of the slope along the failed zone fell below 1.5 with the maximum value of 1.303 for the normal condition. The conducted investigation also indicated that the detachment and propagation of the sliding mass develops much faster with rise in soil moisture content whereas it categorically remains mild during dry seasons. Hence, the driving agent for the occurrence and propagation of the landslide incident was rainfall infiltration and the existence of weak saturated zone at the stated depth.

Determining the suitable settlement areas in Alanya with GIS-based site selection analyses

Urbanization, which is defined as an irreversible global-scale problem nowadays, necessitates the foundation of new settlement areas. In general, no sufficient scientific assessment and analysis were performed during these processes, and thus, various natural disasters cost the loss of many lives and properties every year. Nevertheless, considering the areas that are risky in terms of natural disasters during the selection of settlement areas might prevent a large-scale loss of lives and properties because of natural disasters. Within the scope of this study, it was aimed to determine suitable settlement areas in the Alanya district, which is one of the significant points of interest for tourists in our country and has a large population and new settlement areas because of this increasing population. Within this scope, besides the risks of flood and landslide that are the most important natural disasters in the region, and a forest fire that is among the most significant risks for the region, also the biocomfort zones were included in the assessments. As a result of the study, it was determined that the most important natural disaster risk was flooding in a large portion of the region and that only 6.72% of the study area was suitable for settlement in terms of all the criteria examined in the present study.

Uncovering a 70-year-old permafrost degradation induced disaster in the Arctic, the 1952 Niiortuut landslide-tsunami in central West Greenland

On December 15th 1952, at approximately 14:00 local time a mass of 5.9 × 10⁶ m³ of permafrozen talus deposits failed in a landslide close to the Niiortuut mountain on the south coast of the Nuussuaq peninsula, central West Greenland. Between 1.8 and 4.5 × 10⁶ m³ of the material entered the sea and generated a tsunami that propagated through the Vaigat strait (Sullorsuaq). Here we describe this catastrophic event for the first time by analysis of historical material supplemented by recent fieldwork and discuss the implications for the state of contemporary permafrozen slopes. The tsunami killed a fisherman working on the shore of southern Nuussuaq, 10 km south-east of the landslide. In the mining town of Qullissat, 30 km south of the landslide, it had a runup height of 2.2-2.7 m and caused minor material damage. Morphological evidence show that the basal surface of rupture was 80 m inside the permafrost cemented talus slope, whose degradation was a dynamic conditioning factor for the landslide. The 1952 Niiortuut landslide is the first historically recorded event of permafrost degradation induced landslide-tsunamis in the Arctic. We infer that the landslide and its cascading consequences occurred due to the early-twentieth century warming that started in the late 1910's in the Arctic. Warming is now increasingly affecting this region, as shown by an enhanced recent landslide activity.

Utilization of frequency ratio method for the production of landslide susceptibility maps: Karaburun Peninsula case, Turkey

Geographical information systems (GIS) facilitate both current landslide mapping processes and the prediction of potential landslides that may be experienced in the future. Within the scope of the study, landslide susceptibility maps were created to reduce the damage of possible landslides in the Karaburun Peninsula of İzmir province. A landslide inventory map was produced from related databases in the first place, followed by the creation of parameter maps (elevation, aspect, slope, curvature, land use, vegetation cover, lithology, distance to roads, distance to rivers, and distance to fault lines). The frequency ratio (FR) method was utilized for producing the landslide susceptibility maps on a 5-level risk scale ranging from very low to very high-risk categories. Receiver operating characteristic (ROC) analysis was performed for accuracy testing. The resulting landslide susceptibility map revealed that 3% and 46% of the study area had high- and medium-risk categories, and the low landslide risk areas comprised 47% of the region. These results provide important inputs to guide sustainable strategic and physical planning processes in the region, which has been declared a special protection area and is a popular destination for tourism activities and energy facilities.

Evaluation of potential changes in landslide susceptibility and landslide occurrence frequency in China under climate change

Climate change can alter the frequency and intensity of extreme rainfall across the globe, leading to changes in hazards posed by rainfall-induced landslides. In recent decades, China suffered great human and economic losses due to rainfall-induced landslides. However, how the landslide hazard situation will evolve in the future is still unclear, also because of sparse comprehensive evaluations of potential changes in landslide susceptibility and landslide occurrence frequency under climate change. This study builds upon observed and modelled rainfall data from 24 bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs), a statistical landslide susceptibility model, and empirical rainfall thresholds for landslide initiation, to evaluate changes in landslide susceptibility and landslide occurrence frequency at national-scale. Based on four Shared Socioeconomic Pathways (SSP) scenarios, changes in the rainfall regime are projected and used to evaluate subsequent alterations in landslide susceptibility and in the frequency of rainfall events exceeding empirical rainfall thresholds. In general, the results indicate that the extend of landslide susceptible terrain and the frequency of landslide-triggering rainfall will increase under climate change. Nevertheless, a closer inspection provides a spatially heterogeneous picture on how these landslide occurrence indicators may evolve across China. Until the late 21st century (2080-2099) and depending on the SSP scenarios, the mean annual precipitation is projected to increase by 13.4 % to 28.6 %, inducing an 1.3 % to 2.7 % increase in the modelled areal extent of moderately to very highly susceptible terrain. Different SSP scenarios were associated with an increase in the frequency of landslide-triggering rainfall events by 10.3 % to 19.8 % with respect to historical baseline. Spatially, the southeastern Tibetan Plateau and the Tianshan Mountains in Northwestern Basins are projected to experience the largest increase in landslide susceptibility and frequency of landslide-triggering rainfall, especially under the high emission scenarios. Adaptation and mitigation methods should be prioritized for these future landslide hotspots. This work provides a better understanding of potential impacts of climate change on landslide hazard across China and represents a first step towards national-scale quantitative landslide exposure and risk assessment under climate change.

Changing natural conditions and their impact on the Mt. Śnieżnica landscape, Outer Western Carpathians - Reconstruction of the Holocene environment based on geochemical indices and radiocarbon dating

Environmental changes during the Holocene impacted the development of all civilizations, and it is important to understand the power of this influence through, for instance, the reconstruction of these changes. However, when the climate and environmental conditions of the deep past are analyzed, researchers need to rely on various types of proxy data that are only approximations of the required information. In addition, this type of information is often absent or has several gaps (hiatuses). In the present study, we analyzed a 4.4-m deep core excavated from the fen formed within the landslide body on the northern side of Mt. Śnieżnica in the Wyspowy Beskidy Mountains, the Outer Western Carpathians, southern Poland. In total, we analyzed 405 samples in terms of 29 geochemical components (e.g., nitrogen (N), carbon (C), sulfur (S), and the total organic carbon (TOC)) and physical properties, namely particle-size distribution, loss on ignition (LOI), and microcharcoal content. Additionally, to establish geochronology, we dated 27 samples of different biological materials using the Accelerator Mass Spectrometry radiocarbon method. A detailed examination of plant macrodetritus and wood anatomy supported our interpretation based on the geochemical data. The Mt. Śnieżnica landslide probably formed ca. 14,000 cal BP in the first phase of the Allerød Interstadial. For almost 9000 years, there were no appropriate terrain conditions for the long-term accumulation of organo-mineral materials. At ca. 4400 cal BP, peat accumulation commenced. The beginning of peat accumulation correlates with the global 4.2 Bond event of cold climate conditions. After another ca. 2000 years, the core sediments were dominated by limnetic mud, suggesting aquatic conditions in the landslide depression. This sudden shift in the characteristics of sedimentation is loosely linked to the boundary between the Subboreal and Subatlantic phases (ca. 2500 cal BP). The apparent dichotomy of the depositional record agrees with the reconstructed climatic conditions during the second part of the Holocene. Up to 3000 cal BP, the regional climate was warm and humid, which allowed fast biomass production and hillslope stabilization by trees. Forest fires occurred only at the beginning and end of this period (4400-3000 cal BP). After 3000 cal BP, the regional climate became cool and dry. In this period, we found evidence of intensified erosion, but it was unrelated to forest fire activity.

Spatio-temporal landslide inventory and susceptibility assessment using Sentinel-2 in the Himalayan mountainous region of Pakistan

The 2005 Kashmir earthquake has triggered widespread landslides in the Himalayan mountains in northern Pakistan and surrounding areas, some of which are active and are still posing a significant risk. Landslides triggered by the 2005 Kashmir earthquake are extensively studied; nevertheless, spatio-temporal landslide susceptibility assessment is lacking. This can be partially attributed to the limited availability of high temporal resolution remote sensing data. We present a semi-automated technique to use the Sentinel-2 MSI data for co-seismic landslide detection, landslide activities monitoring, spatio-temporal change detection, and spatio-temporal susceptibility mapping. Time series Sentinel-2 MSI images for the period of 2016-2021 and ALOS PALSAR DEM are used for semi-automated landslide inventory map development and temporal change analysis. Spectral information combined with topographical, contextual, textural, and morphological characteristics of the landslide in Sentinel-2 images is applied for landslide detection. Subsequently, spatio-temporal landslide susceptibility maps are developed utilizing the weight of evidence statistical modeling with seven causative factors, i.e., elevation, slope, geology, aspect, distance to fault, distance to roads, and distance to streams. The results reveal that landslide occurrence increased from 2016 to 2021 and that the coverage of areas of relatively high susceptibility has increased in the study area.

Identification of precipitation trend and landslide susceptibility analysis in Miandoab County using MATLAB

Iranian plateau is a seismically active region. Within this region, northwestern Iran is of high importance. Before proper planning for mitigating the earthquake-induced hazards can be achieved, it is necessary to identify high-risk areas in terms of susceptibility to earthquakes. In this study, landslide susceptibility in Miandoab Country was modeled using the so-called random forest algorithm (RFA) in MATLAB based on records acquired at 67 earthquake hotspots considering 9 factors affecting the earthquake occurrence (i.e., height, slope, direction, distance from fault, distance from river, distance from road, land use, geology, and precipitation). Predictive power of the model and validity of its results were evaluated using relative operating characteristic (ROC) curve and area under the curve (AUC). The assessment results showed very good accuracy of the model (0.97). It was further found that digital height layer, geology, and distance from fault impose the largest contributions into earthquake potential. The results also showed that 53%, 8.3%, and 38.4% of the study area were classified as being at low risk, moderate risk, and high risk of earthquake. Among other climatic elements, the precipitation exhibits the largest fluctuations; we proceeded to evaluate precipitation trends in the study area for a statistical period of 30 years. This was practiced by implementing Mann-Kendall nonparametric test in MATLAB. This subject-matter is especially important in Iran where the annual precipitation level is as low as 250 mm. The results showed that the precipitation follows an increasing trend in the region.

GIS-based multicriteria decision analysis for settlement areas: a case study in Canik

In addition to global population growth due to migration from rural areas to urban areas, population density is constantly increasing in certain regions, thereby necessitating the introduction of new settlements in these regions. However, in the selection of settlement areas, no sufficient preliminary examinations have been conducted; consequently, various natural disasters may cause significant life and property losses. Herein, the most suitable settlement areas were determined using GIS (geographic information systems) in Canik District, where the population is continuously increasing. Therefore, this study aimed to incorporate a new perspective into studies on this subject. Within the scope of the study, landslide and flood risks, which are among the most important natural disasters in the region, were primarily evaluated, and high-risk areas were determined. Elevation, slope, aspect, curvature, lithology, topographic humidity index (TWI), and proximity to river parameters were used to produce flood susceptibility maps. A digital elevation model (DEM) of the study area was produced using contours on the 1/25,000 scaled topographic map. The elevation, slope, aspect, curvature, and TWI parameters were produced from the DEM using the relevant analysis routines of ArcGIS software. The raster map of each parameter was divided into 5 subclasses using the natural breaks classification method. In the reclassified raster maps, the most flood-sensitive or flood-prone subclasses were assigned a value of 5, and the least sensitive subclasses were assigned a value of 1. Then, the reclassified maps of the 7 parameters were collected using the "map algebra" function of ArcGIS 10.5 software, and the flood susceptibility index (FSI) map of the study area was obtained. The flood susceptibility map of the study area was obtained by dividing the FSI into 5 subclasses (very low, low, moderate, high, and very high) according to the natural breaks classification method. Thereafter, suitable and unsuitable areas in terms of biocomfort, which affects people's health, peace, comfort, and psychology and is significant in terms of energy efficiency, were determined. At the last stage of the study, the most suitable settlement areas that were suitable in terms of both biocomfort and low levels of landslide and flood risks were determined. The calculated proportion of such areas to the total study area was only 2.1%. Therefore, because these areas were insufficient for the establishment of new settlements, areas that had low landslide and flood risks but were unsuitable for biocomfort were secondarily determined; the ratio of these areas was calculated as 56.8%. The remaining areas were inconvenient for the establishment of settlements due to the risk of landslides and floods; the ratio of these areas was calculated as 41.1%. This study is exemplary in that the priority for the selection of settlement areas was specified, and this method can be applied for selecting new settlements for each region considering different criteria. Due to the risk of landslides or flooding in the study area, the areas unsuitable for establishing a settlement covered approximately 41.1% of the total study area. The areas that had low flood and landslide risks but were suitable for biocomfort constituted only 2.1% of the study area. In approximately 56.8% of the study area, the risk of landslides or floods was low, and these areas were unsuitable in terms of biocomfort. Therefore, these areas were secondarily preferred as settlement areas. The most suitable areas for settlements constituted only 0.19% of the total study area, and these areas will not be able to meet the increasing demand for settlement area. Therefore, it is recommended to select areas that do not have the risk of landslides and floods but are unsuitable for biocomfort. This study reveals that grading should be performed in the selection of settlement areas. When choosing a settlement area in any region, possible natural disasters in the region should be identified first, and these disasters should be ordered in terms of their threat potential. Moreover, biocomfort areas suitable for settlements should be considered. In the next stages of settlement area selection, the criteria that affect the peace and comfort of people, such as distance to pollution sources, distance to noise sources, and proximity to natural areas, should also be evaluated. Thus, a priority order should be created for the selection of settlement areas using various other criteria.

GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya

Landslides are one the most destructive and life-endangering hazard in the Darjeeling Himalayan region and keeping in mind the interest of society and their future prospects identification of landslide potential areas is a very pertinent task in this area. Therefore, the present study aimed toward the landslide susceptibility zonation (LSZ) mapping in and around the Kalimpong region by applying Analytic Hierarchy Process (AHP) method integrated with fifteen causative factors including slope, lineament, drainage density, land use land cover, relative relief, soil texture, lithology, elevation, aspect, thrust and faults, plan curvature, profile curvature, road network, topographic wetness index and stream power index. Tolerance and variance inflation factors with Pearson's correlation coefficient are used to assess potential collinearity among the selected factors, and subsequently, the final model has been constructed by enduring an acceptable consistency ratio (<0.10). Thereafter, to classify this region into very low, low, moderate, high and very high susceptible zones quantile, geometric interval, Jenk's natural break and success rate curve (SRC) techniques are adopted to compare and check the optimum LSZ categorization. Considering the identified 647 landslides, Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curve is used to gauge the best LSZ map. The AUC ROC shows SRC method (m = 0.9) yields the highest result, achieving a prediction accuracy of 79.5% and, therefore, is considered the most promising LSZ form for the present study area. The results obtained from the study highlight the spatial information of areas that may face slope instability and helps government agencies, stakeholders for drafting adequate measures due to absence of proper landslide early warning systems in this region.

Chemical weathering and progressing alteration as possible controlling factors for creeping landslides

Landslides can behave as dynamic processes, which emerge from the complex interplay of tectonics, erosion, weathering and gravitational influences, triggered by various hydrological, mineralogical, biological and geotechnical factors. Integral studies to assess the mechanisms underlying landslide initiation and progression are mainly focussed on specific cases with high geohazard potential. The landslide near Stadtschlaining (Austria) represents a key study site to elucidate the impacts of pelitic sediment composition, weathering regime, alteration patterns and hydrochemistry on recurrent damage progression in the local infrastructure. Based on field work, soil-mechanical logging (Atterberg limits, undrained strength, friction angles), water chemistry (ICP-OES, IC, hydrochemical modeling), solid-phase characterization (XRD, XRF, SEM) and sorption experiments we establish a conceptual model for initiating and progressing of landslides: Infiltration of low mineralized meteoric water (EC: <200 μS/cm) in permeable limonitic gravels triggers chemical weathering of greenschist-derived detritus and promotes its transformation into kaolinite and smectite. The clayey strata (>50 wt% of clay minerals) create zones of mechanical and chemical weakness in the underground (~4-6 m below ground level), which are characterized by particle disintegration/delamination, slip bedding and deformations, and development of porous layers depicting water flow paths. Subsequent Na⁺ exchange for bivalent ions in the smectite interlayer delivered by percolating, highly mineralized water (EC: 1600-5100 μS/cm) is caused by de-icing salt and fertilizer applications during winter and late summer, and yield in i) decohesion and physical breakdown of the particle aggregates and ii) swelling of the clay matrix in early spring and autumn. These processes reduce the shear strength of the pelitic sediments, resulting in failure and initiation of landslides (deformation: ~500 mm within a month) and subsequent steady creeping motion (deformation: ~100 mm in 6 months). Customized engineered solutions to prevent landslides in this area are presented, which can be conveyed to analogous landslide-affected areas worldwide.

Acid rain: an unsuspected factor predisposing Panzhihua airport landslide, China

Understanding the effect of acid rain to landslides is crucial for a better landslide risk assessment. This work aims to reveal the unsuspected but key role of acid rain in Panzhihua airport landslide, China. Firstly, we propose a hypothesis that acid rain may aggravate the slaking behavior of mudstone at weak interlayer and make it more fragmented, eventually further reducing its shear strength and predisposing the Panzhihua airport landslide. Subsequently, mudstone samples are subjected to slaking durability test, respectively, using water with a pH of 7 and two dilute hydrochloric acid solution with pH of 5 and 3. Slaking durability index (I_dn) is adopted aiming to quantitatively evaluate the impact of acid rain on the slaking. Moreover, the mechanisms of acid rain affecting the slaking behavior of mudstone are revealed by (1) analyzing cation compositions changes in different pH slaking fluid and (2) observing micro-structure change of mudstone-chip before and after acid rain treatment. Finally, three works are conducted as evidences to prove that acid rain indeed plays a key role in the occurrence of Panzhihua airport landslide, including (1) analysis of the link between the slaking behavior of mudstone and its shear strength, (2) comparison of cations between spring water at the edge of the toe of landslide and acid rain, and (3) comparison of mineral contents of mudstone samples collected from different locations. These findings have implications for comprehensively analyzing the formation mechanism of landslide in acid rain area (such as Europe, North America, and China).

Anti-erosion rehabilitation: Effects of revegetation method and site traits on introduced and native plant cover and richness

In mountainous areas, bare slopes represent often dangerous forms of land degradation that need to be rehabilitated. However, revegetation is usually performed with non-native plants, negatively impacting the natural landscape value. Comprehensive studies, especially on poorly investigated long-term revegetation effects, are therefore essential for the improvement of rehabilitation practices. In this study, four landslides or disused quarries surrounded by well-preserved (semi-) natural vegetation that were revegetated between 1988 and 2002 with non-native only herbaceous or both herbaceous and woody plants were studied 1-9 and 16-31 years after rehabilitation. A total of 111 sampling areas were surveyed for introduced and volunteer species and other important ground cover soil and topography traits. Climatic traits and species' ecological indicators were retrieved from the available databases. The time patterns and ecological spectra of the plant covers were analysed and correlated to the site traits. In the first decade, introduced plants were initially abundant but decreased rapidly. In the long-term, the more stress-tolerant among them still had a considerable cover, especially in fine-textured soils. Native species were established slowly but, due to their higher stress tolerance, dominated in the long-term, especially at more stressful sites. In areas with predominantly herbaceous cover, soil factors, such as gravel content, were more related to the plant cover. In areas with predominantly woody plant cover, the high plant cover was dependent on microclimatic factors, especially aspect. At sites like those studied here, woody species should be always used, but in the form of native plants, as they tend to persist. If herbaceous species are unavailable in native forms, their introduction should be avoided in areas not exposed to erosion, whereas poorly stress-tolerant non-native plants should be used in steep areas as they create a fast but short-lasting cover compensated in the long term by the establishment of native species.

Exploitation of optical and SAR amplitude imagery for landslide identification: a case study from Sikkim, Northeast India

Detection and mapping of landslides is one of the most important techniques used for reducing the impact of natural disasters especially in the Himalaya, owing to its high amount of tectonic deformation, seismicity, and unfavorable climatic conditions. Moreover, the northeastern part of the Himalaya, severely affected by landslides every monsoon, is poorly studied. The information on the inventories is inhomogeneous and lacking. In this context, satellite-based earth observation data, which has significantly advanced in the last decade and often serves as a potential source for data collection, monitoring, and damage assessment for disasters in a short time span, has been implemented. Keeping in mind the above framework, this study aims to exploit the potentials of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery for identifying new landslides in vegetated and hilly areas of the northeastern part of India. In order to assess the potentials of our data and methodology, a landslide event which occurred on 13 August 2016 13:30 h (IST) in North Sikkim, India, triggered due to rainfall has been explored in detail. The landslide also resulted in the formation of a lake, 2.2 km in length and 290 m in width. Difficulty in procurement of cloud-free datasets immediately after the event led us to the use of Sentinel-1 SAR backscatter data, to assess its potential for this purpose. It is observed that the potential of SAR amplitude imagery is limited to different aspects as per the sensor look direction during the mode of acquisition. Furthermore, the present study also incorporates a change detection algorithm to evaluate the performance of the Sudden Landslide Identification Product (SLIP) model to identify new landslides using Sentinel-2 multispectral imagery. Overall, the results exhibit that integrated usage of both optical and SAR amplitude imagery may provide a plethora of information for identification and mapping of new landslides for damage assessment and early warning. All the above results combined together suggest this method for rapid identification of landslides in the Himalayan terrain with special emphasis on the northeastern part of the Himalaya. The automation of this method for future operational usage is also suggested.

SitkaNet: A low-cost, distributed sensor network for landslide monitoring and study

Landslides threaten the infrastructure and safety of communities. Soil conditions can predict landslide threat, but the cost and complexity of sensing systems for documenting hazardous conditions across a heterogeneous spatial area prevent widespread utilization. The SitkaNet system is a low-cost, easier to install alternative that allows for numerous sites to be monitored with real-time reporting and expands the accessibility of data-driven landslide forecasting. Using a combination of industry-proven sensors and cheaper alternatives, each SitkaNet node can measure the rainfall, six soil moisture sensors at varying depths, water table, atmospheric pressure, humidity, and temperature at each site for less than one-fifth the cost of existing solutions (<$1000/node). The SitkaNet nodes transmit data wirelessly at five-minute intervals over LoRa network to an Ethernet connected hub instead of more traditional on-site cellular or satellite methods. The node electronics are packaged with 3D printed components in a small waterproof case mounted on a hand-driven well-point utilized for the water level measurement. Each node is intended for operation for more than six months on a lithium-ion battery pack: no solar panel is needed, so amenable to low-light sites. The installation process is streamlined which allows for a node to be installed in less than a day compared to multi-day procedures required by other systems.

Field reconnaissance data from GEER investigation of the 2018 MW 7.5 Palu-Donggala earthquake

The M_w7.5 Palu-Donggala earthquake occurred on 28 September 2018 and caused significant damage in Palu City and the surrounding Central Sulawesi region of Indonesia. The earthquake initiated a series of catastrophic landslides (classified as flowslides) [1,2], collapsed buildings, and generated tsunami waves that impacted Palu Bay's coast. The earthquake claimed over 4000 lives, making it the deadliest natural disaster of 2018. We performed a post-earthquake field reconnaissance and collected perishable data at the sites of five significant flowslides (named for the communities where they occurred: Balaroa, Petobo, Lolu Village, Jono Oge, and Sibalaya), as well as at other damage locations in the mesoseismal region. Our field team consisted of five U.S.-based members, who were sponsored by the U.S. National Science Foundation-supported Geotechnical Extreme Events Reconnaissance (GEER) organization [3], in collaboration with geologists, geotechnical engineers, and other researchers from Indonesia's Center for Earthquake Studies (PusGen) and the Indonesian Society of Geotechnical Engineers (HATTI) [this international team is collectively referred to as the Palu Earthquake “;GEER” team]. The GEER team arrived at Palu City on 13 November 2018 and conducted five days of extensive fieldwork using instrumentation from the Natural Hazards Reconnaissance Facility (known as the “RAPID”) [4,5], including mobile data collection software, digital imaging systems, high-resolution Global Navigation Satellite System (GNSS) antennas, and unmanned aerial vehicles (UAVs, or “;drones”). The resulting dataset includes over 2000 geotagged photographs, UAV images, ground coordinates, and other field measurements and observations, as well as associated post-processed geospatial data products (point clouds, digital surface models, orthomosaic images). Additionally, we used remote sensing data (i.e., pre- and post-event satellite imagery) to generate displacement vectors for over 1200 structures affected by the flowslides. The complete reconnaissance dataset is openly available on DesignSafe [6]. The data collected by the field team and subsequent mapping efforts, which document the morphology and patterns of movements of the flowslides, may be used by researchers studying liquefaction-induced flowslides. In addition, the displacement mapping provides a unique dataset for researchers who are calibrating and verifying simulation models of landslide displacements, or who are seeking a validation dataset for image correlation analysis (including machine learning routines). This dataset is associated with original research presented in “;East Palu Valley Flowslides Induced by the 2018 M_W 7.5 Palu-Donggala Earthquake” [1] and also is the basis of research presented by Gallant et al. [2].

Field testing innovative differential geospatial and photogrammetric monitoring technologies in mountainous terrain near Ashcroft, British Columbia, Canada

This paper presents a novel approach to continuously monitor very slow-moving translational landslides in mountainous terrain using conventional and experimental differential global navigation satellite system (d-GNSS) technologies. A key research question addressed is whether displacement trends captured by a radio-frequency "mobile" d-GNSS network compare with the spatial and temporal patterns in activity indicated by satellite interferometric synthetic aperture radar (InSAR) and unmanned aerial vehicle (UAV) photogrammetry. Field testing undertaken at Ripley Landslide, near Ashcroft in south-central British Columbia, Canada, demonstrates the applicability of new geospatial technologies to monitoring ground control points (GCPs) and railway infrastructure on a landslide with small and slow annual displacements (<10 cm/yr). Each technique records increased landslide activity and ground displacement in late winter and early spring. During this interval, river and groundwater levels are at their lowest levels, while ground saturation rapidly increases in response to the thawing of surficial earth materials, and the infiltration of snowmelt and runoff occurs by way of deep-penetrating tension cracks at the head scarp and across the main slide body. Research over the last decade provides vital information for government agencies, national railway companies, and other stakeholders to understand geohazard risk, predict landslide movement, improve the safety, security, and resilience of Canada's transportation infrastructure; and reduce risks to the economy, environment, natural resources, and public safety.

Disaster risk reduction in conflict contexts: Lessons learned from the lived experiences of Rohingya refugees in Cox's Bazar, Bangladesh

Bangladesh is currently hosting more than one million stateless Rohingya refugees, who fled from the Rakhine State to avoid genocide and serious crimes against humanity persecuted by the Myanmar Army. The newly arrived Rohingyas were accommodated in overcrowded refugee camps in Cox's Bazar District (CBD). The camps are highly vulnerable to landslides, tropical cyclones, flash-flooding, and communicable disease outbreak. Although a number of improvement measures are ongoing, however, no study to date has addressed Rohingyas' self-adopted strategies to mitigate disaster risks. Consequently, this paper aims to explore how refugees cope with risks associated with environmental hazards in the Kutupalong Rohingya Camp in CBD. A mixed-methods research strategy incorporating both quantitative household questionnaire survey and qualitative focus group discussions (FGDs) techniques were applied. In total 250 Rohingya refugees were selected for the questionnaire survey using a stratified random sampling method from camps 17 and 19, and two FGDs (male and female-only) were carried out in camp 13 involving 21 Rohingya participants. Results derived from the study show that responding to early warning systems, storing dried food and medicine, utilising available resources, relocating to safer shelters, and keeping hopes high were some of the coping strategies practised by the respondents. Literacy level imposed a significant impact over respondents' perception to accept various measures. For instance, the probability of storing dried food in preparation for disasters was 4 times higher among literate Rohingya compared to their illiterate counterparts. Similarly, for literate respondents, the probability was 20 times higher to store medicine than for illiterate. Guaranteed distribution of shelter strengthening kits among all refugee households, the inclusion of disaster risk awareness and preparedness training, ensuring safe and dignified return in Myanmar, and global and regional cooperation to address the refugee crisis are some of the propositions recommended in this study for improving Rohingyas' future adaptation strategies in a humanitarian context.

Distinct types of landslides in moraines associated with the post-LIA glacier thinning: Observations from the Kinzl Glacier, Huascarán, Peru

The Kinzl Glacier is a unique dendritic-type glacier of the Peruvian Cordillera Blanca and is surrounded by well-developed Little Ice Age (LIA) moraines. Based on field observations and analysis of historical photographs (since 1932) and remotely sensed images (since 1948), we interpret distinct mechanisms of landslides in these moraines and link them to glacier retreat and thinning. Three types of landslides are distinguished according to the cross-profile morphology: (i) type "N", (ii) type "M" and (iii) type "A". Our data show that sliding of type "N" is an ice-contact slope failure that occurs as a gradual process simultaneously to glacier downwasting. In contrast, type "A" can occur at any time once the glacier has downwasted below the sliding plane and cannot buttress the nearly vertical inner slopes of the moraine anymore. We further argue that the type "M" can gradually evolve from type "N" or can occur as an episodic event. Probably due to overconsolidation of moraine material, landslides of types "N" and "M" keep their shape during sliding and move in form of several hundred meters long unbroken blocks. In contrast type "A" is internally disintegrated during landsilding. All investigated landslide types are characterized by increased width-length ratio and movement perpendicular to the direction of the flow of the glacier. We opine that the occurrence of these landslide types is directly or indirectly associated with glacial ice loss occurring since the end of the LIA. The observed landslides in the LIA moraines of the Kinzl Glacier are unique in the regional context considering their estimated size on the order of 10⁶ m³ and contribute significantly to the paraglacial adjustment of moraine slopes and landform evolution in the post-LIA context. Apart from their role in moraine evolution, these landslides can trigger hazardous cascading process-chains in high-alpine environments.

Sendai voluntary commitments: landslide stakeholders and the all-of-society approach enhanced by UNDRR

The resilience of communities and nations is a necessary condition for sustainable development. Building resilience, however, is not always a straightforward process and requires joint efforts, an all-of-society approach. Thus, the commitment, goodwill, knowledge, experience, and resources of all stakeholders contributing to disaster risk reduction (DRR) are crucial. In UNDRR's Sendai Framework Voluntary Commitments online platform, the work of all stakeholders can be showcased and tracked. Using novel data from this platform, this article presents descriptive information about the types of commitments made by stakeholders working on landslides. Results suggest that landslide is the third most covered hazard. Commitments working on this hazard have a more balanced distribution of global, regional, and local actions as compared with the whole sample. Also, landslide commitments tend to display higher levels of collaboration (as measured by the number of organizations involved) and longer duration (a commitment will last 7.6 years on average). Common issues being addressed include capacity development, risk management, and community-based DRR. When looking at specific regions and countries, there are opportunities for increased partnerships and effectiveness in topics such as knowledge sharing and technology solutions. The systemic nature of risks is increasingly apparent, and this article may stimulate further studies analyzing complexity and the joint action of all stakeholders committed to accelerate the implementation of the Sendai Framework.

Unbiased logic-tree data for earthquake-induced landslide hazard maps for low-to-moderate magnitude events

Land-use planning in regard of earthquake-triggered landslides is usually implemented by means of the production of hazard maps. The well-known Newmark rigid block methodology is the most frequent used approach for this purpose. In this method, slope stability is evaluated by the estimation of the Newmark displacement, which is used to set different categories of hazard. This methodology presents limitations due to the difficulty of incorporating the variability of the used variables. For that reason, the logic-tree approach has been used in order to incorporate the epistemic uncertainties and compute probabilistic seismic-landslide hazard maps. However, the used weights in the logic-tree are usually set for each branch based on an expert judgement or subjective criteria. This article provide data obtained from the use of logic-tree methodology; this dataset is useful for deriving the unbiased weights to use in such methodology and in moderate-to-low magnitude scenarios. The data presented here are related to the article entitled "Obtaining suitable logic-tree weights for probabilistic earthquake-induced landslide hazard analyses" (Rodríguez-Peces et al., 2020) [1].

Surf zone microbiological water quality following emergency beach nourishment using sediments from a catastrophic debris flow

Urban disaster response requires disposal of complex wastes. This study regards a case wherein high intensity rainfall fell over a remote mountainous area previously burned by wildfire, generating debris flows that devastated a downstream town. Sanitary sewers and homes with septic systems were damaged, releasing human waste into the debris flow field. Contaminated sediments, with their high fecal indicator bacteria (FIB) concentrations, were cleared from public rights-of-way and creek channels by local authorities, then disposed onto distant Goleta Beach for beach nourishment, causing immediate surf zone microbiological water quality exceedances. To determine potential public health threats, disposed sediments and surf zone waters were sampled and analyzed-relative to reference samples of mountain soil and raw sewage-for FIB, pathogens, human (HF183) and other host- (Gull2 TaqMan, and DogBact) associated DNA-based fecal markers, and bacterial community 16S rRNA gene sequences. Approximately 20% of disposed sediment samples contained the HF183 marker; sequencing suggested that all samples were contaminated by sewage. In an initial sediment disposal period, surf zone waters harbored intestinal bacterial sequences that were shared with disposed sediments and sewage. Yet surf zone bacterial communities returned to mostly marine clades within weeks. Taken together, multiple conventional and DNA-based analyses informed this forensic assessment of human waste contamination. In the future, similar analyses could be used earlier in disaster response to guide sediment disposal decisions towards continuously protecting beachgoer health.

Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble

The major target of this study is to design two novel hybrid integration artificial intelligent models, which are denoted as LADT-Bagging and FPA-Bagging, for modeling landslide susceptibility in the Youfanggou district (China). First of all, we prepared a geospatial database in the study area, including 79 landslide points that were divided into a training and validating dataset and 14 landslide conditioning factors. Second, the Support Vector Machines classifier (SVMC) approach was adapted to analyze the predictive capability of the landslide predisposing factors in each method. Then, a multicollinearity analysis using TOL and VIF parameters and Pearson's correlation coefficient methods were applied to verify the multicollinearity and correlation between these factors. Third, the LADT-Bagging and FPA-Bagging models were built by the integration of the LogitBoost alternating decision trees (LADT) with the Bagging ensemble and Forest by Penalizing Attributes (FPA) with the Bagging ensemble, respectively. Besides, heuristic tests were also applied to identify the appropriate values of each model's parameters in order to obtain the best programmer. Finally, for the training dataset, the results reveal that the LADT-Bagging model acquire the largest AUC value (0.980), smallest standard error (SE) (0.0134), narrowest 95% confidence interval (CI) (0.920-0.999), highest accuracy value (AV) (91.03%), highest specificity (94.44%), highest sensitivity (88.10%), highest F-measure (0.9115), lowest MAE (0.2016), lowest RMSE (0.2653), and highest Kappa (0.8205). About the result of validating dataset, it reveal that the LADT-Bagging model acquire the largest AUC value (0.781), the smallest SE (0.0539), the narrowest 95% CI (0.673-0.867), highest AV (71.19%), highest specificity (74.29%), highest sensitivity (69.77%), highest F-measure (0.7195), lowest MAE (0.3509), lowest RMSE (0.4335), and highest Kappa (0.4359). The results indicate that the LADT-Bagging model outperforms the FPA-Bagging, LADT and FPA models. Furthermore, the results of a Wilcoxon signed-rank test demonstrate that LADT-Bagging is significantly statistically different from other models. Therefore, in this study, the proposed new models are useful tools for land use planners or governments in high landslide risk areas.

Attribute selection using correlations and principal components for artificial neural networks employment for landslide susceptibility assessment

Landslide susceptibility maps can be developed with artificial neural networks (ANNs). In order to train our ANNs, a digital elevation model (DEM) and a scar map of one previous event were used. Eleven attributes are generated, possibly containing redundant information. Our base model is formed by, essentially, one input (the DEM), eleven attributes, 30 neurons, and one output (susceptibility). Principal components (PCs) group information in the first projected variables, the last ones can be expendable. In the present paper, four groups of models were trained: one with eleven attributes generated from the DEM; one with 8 out of 11 attributes, in which 3 were eliminated by their high correlation with others; other, with the data projected over its PCs; and another, using 8 out of 11 PCs. The used number of neurons in hidden layer is 30, calibrated based on a complexity analysis that is an in-house developed method. The ANN models trained with the original data generated better statistical results than their counterparts trained with the PC transformed input. Keeping the original 11 attributes calculated provided the best metrics among all models, showing that eliminating attributes also eliminates information used by the model. Using 11 PC transformed attributes hindered trained. However, for the model with eight PCs, training was much faster than its counterpart with little accuracy loss. The metrics and maps achieved were considered acceptable, conveying the power of our model based on ANNs, which uses essentially one input (the DEM) for mapping areas susceptible to mass movements.

Observations on the May 2019 Joffre Peak landslides, British Columbia

Two catastrophic landslides occurred in quick succession on 13 and 16 May 2019, from the north face of Joffre Peak, Cerise Creek, southern Coast Mountains, British Columbia. With headscarps at 2560 m and 2690 m elevation, both began as rock avalanches, rapidly transforming into debris flows along middle Cerise Creek, and finally into debris floods affecting the fan. Beyond the fan margin, a flood surge on Cayoosh Creek reached bankfull and attenuated rapidly downstream; only fine sediment reached Duffey Lake. The toe of the main debris flow deposit reached 4 km from the headscarp, with a travel angle of 0.28, while the debris flood phase reached the fan margin 5.9 km downstream, with a travel angle of 0.22. Photogrammetry indicates the source volume of each event is 2-3 Mm³, with combined volume of 5 Mm³. Lidar differencing, used to assess deposit volume, yielded a similar total result, although error in the depth estimate introduced large volume error masking the expected increase due to dilation and entrainment. The average velocity of the rock avalanche-debris flow phases, from seismic analysis, was ~ 25-30 m/s, and the velocity of the 16 May debris flood on the upper fan, from super-elevation and boulder sizes, was 5-10 m/s. The volume of debris deposited on the fan was ~ 10⁴ m³, 2 orders of magnitude less than the avalanche/debris flow phases. Progressive glacier retreat and permafrost degradation were likely the conditioning factors; precursor rockfall activity was noted at least ~6 months previous; thus, the mountain was primed to fail. The 13 May landslide was apparently triggered by rapid snowmelt, with debuttressing triggering the 16 May event.

The human cost of global warming: Deadly landslides and their triggers (1995-2014)

Worldwide, landslides incur catastrophic and significant economic and human losses. Previous studies have characterized the patterns in landslides' fatalities, from all kinds of triggering causes, at a continental or global scale, but they were based on data from periods of <10 years. The research herein presented hypothesizes that climate change associated with extreme rainfall and population distribution is contributing to a higher number of deadly landslides worldwide. This study maps and identified deadly landslides in 128 countries and it encompasses their role, for a 20 years' period from January/1995 to December/2014, considered representative for establishing a relationship between landslides and their meteorological triggers. A database of georeferenced landslides, their date, and casualties' information, duly validated, was implemented. A hot spot analysis for the daily record of landslide locations was performed, as well as a percentile-based approach to evaluate the trend of extreme rainfall events for each occurrence. The relationship between casualty, population distribution, and rainfall was also evaluated. For 20 years, 3876 landslides caused a total of 163,658 deaths and 11,689 injuries globally. They occurred most frequently between June and December in the Northern Hemisphere, and between December and February in the Southern Hemisphere. A significant global rise in the number of deadly landslides and hotspots across the studied period was observed. Analysis of daily rainfall confirmed that more than half of the events were in areas exposed to the risk of extreme rainfall. The relationships established between extreme rainfall, population distribution, seasonality, and landslides provide a useful basis for efforts to model the adverse impacts of extreme rainfall due to climate change and human activities and thus contribute towards a more resilient society.

Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa

Application of suitable methods to generate landslide susceptibility maps (LSM) can play a key role in risk management. Rwanda, located in centre-eastern Africa experiences frequent and intense landslides which cause substantial impacts. The main aim of the current study was to effectively generate susceptibility maps through exploring and comparing different statistical and probabilistic models. These included weights of evidence (WoE), logistic regression (LR), frequency ratio (FR) and statistical index (SI). Experiments were conducted in Rwanda as a study area. Past landslide locations have been identified through extensive field surveys and historical records. Totally, 692 landslide points were collected and prepared to produce the inventory map. This was applied to calibrate and validate the models. Fourteen maps of conditioning factors were produced for landslide susceptibility modeling, namely: elevation, slope degree, topographic wetness index (TWI), curvature, aspect, distance from rivers and streams, distance to main roads, lithology, soil texture, soil depth, topographic factor (LS), land use/land cover (LULC), precipitation and normalized difference vegetation index (NDVI). Thus, the produced susceptibility maps were validated using the receiver operating characteristic curves (ROC/AUC). The findings from this study disclosed that prediction rates were 92.7%, 86.9%, 81.2% and 79.5% respectively for WoE, FR, LR and SI models. The WoE achieved the highest AUC value (92.7%) while the SI produced a lowest AUC value (79.5%). Additionally, 20.42% of Rwanda (5048.07 km²) was modeled as highly susceptible to landslides with the western part the highly susceptible comparing to other parts of the country. Conclusively, the comparison of produced maps revealed that all applied models are promising approaches for landslide susceptibility studying in Rwanda. The results of the present study may be useful for landslide risk mitigation in the study area and in other areas with similar terrain and geomorphological conditions. More studies should be performed to include other important conditioning factors that exacerbate increases in susceptibility especially anthropogenic factors.

Empirical investigation of friction weakening of terrestrial and Martian landslides using discrete element models

Understanding what controls the travelling distance of large landslides has been the topic of considerable debate. By combining observation and experimental data with depth-averaged continuum modelling of landslides and generated seismic waves, it was empirically observed that lower effective friction had to be taken into account in the models to reproduce the dynamics and runout distance of larger volume landslides. Moreover, such simulation and observation results are compatible with a friction weakening with velocity as observed in earthquake mechanics. We investigate here as to whether similar empirical reduced friction should be put into discrete element models (DEM) to reproduce observed runout of large landslides on Earth and on Mars. First we show that, in the investigated parameter range and for a given volume, the runout distance simulated by 3D DEM is not much affected by the number (i.e. size) of grains once this number attains ~ 8000. We then calibrate the model on laboratory experiments and simulate other experiments of granular flows on inclined planes, making it possible for the first time to reproduce the observed effect of initial volume and aspect ratio on runout distances. In particular, the normalised runout distance starts to depend on the volume involved only above a critical slope angle > 16-19°, as observed experimentally. Finally, based on field data (volume, topography, deposit), we simulate a series of landslides on simplified inclined topography. The empirical friction coefficient, calibrated to reproduce the observed runout for each landslide, is shown to decrease with increasing landslide volume (or velocity), going down to values as low as 0.1-0.2. No distinguishable difference is observed between the behaviour of terrestrial and Martian landslides.

Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan

The Bostanlik district, Uzbekistan, is characterized by mountainous terrain susceptible to landslides. The present study aims at creating a statistically derived landslide susceptibility map - the first of its type for Uzbekistan - for part of the area in order to inform risk management. Statistical index (SI), frequency ratio (FR) and certainty factor (CF) are employed and compared for this purpose. Ten predictor layers are used for the analysis, including geology, soil, land use and land cover, slope, aspect, elevation, distance to lineaments, distance to faults, distance to roads, and distance to streams. 170 landslide polygons are mapped based on GeoEye-1 and Google Earth imagery. 119 (70%) out of them are randomly selected and used for the training of the methods, whereas 51 (30%) are retained for the evaluation of the results. The three landslide susceptibility maps are split into five classes, i.e. very low, low, moderate, high, and very high. The evaluation of the results obtained builds on the area under the success rate and prediction rate curves (AUC). The training accuracies are 82.1%, 74.3% and 74%, while the prediction accuracies are 80%, 70% and 71%, for the SI, FR and CF methods, respectively. The spatial relationships between the landslides and the predictor layers confirmed the results of previous studies conducted in other areas, whereas model performance was slightly higher than in some earlier studies - possibly a benefit of the polygon-based landslide inventory.

The western Japan chaotic rainstorm disaster: a brief report from Hiroshima

The western Japan rainstorm disaster on July 6, 2018, was one of the most serious natural catastrophes in Japan, excluding earthquake events. Its main characteristics were severe and widespread flooding and landslides which cut off many areas, interrupting both traffic and telecommunication, and loss of clean water supply. We explored the utility of unmanned aerial vehicles to collect precise information on traffic disruption and damage to hospitals for patient rescue and for efficient allocation of resources. This visualized information was beneficial for determining rescue strategies. Lessons from this disaster and novel technologies could contribute to minimizing damage in future disasters.

Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling

The main aim of the present study is to explore and compare three state-of-the art data mining techniques, best-first decision tree, random forest, and naïve Bayes tree, for landslide susceptibility assessment in the Longhai area of China. First, a landslide inventory map with 93 landslide locations was randomly divided, with 70% of the area used for training landslide models and 30% used for the validation process. A spatial database of 14 conditioning factors was constructed under a geographic information system environment. Subsequently, the ReliefF method was employed to assess the prediction capability of the conditioning factors in landslide models. Multicollinearity of these factors was verified using the variance inflation factor, tolerance, and Pearson's correlation coefficient. Finally, the three resulting models were evaluated and compared using the area under the receiver operating characteristic (AUROC) curve, standard error, 95% confidence interval, accuracy, precision, recall, and F-measure. The random forest model showed the AUROC values (0.869), smallest standard error (0.025), narrowest 95% confidence interval (0.819-0.918), highest accuracy value (0.774), highest precision (0.662), and highest F-measure (0.662) for the training dataset. Thus, the random forest model is a promising technique that could be used for landslide susceptibility mapping.

Contaminated landslide runout deposits in rivers - Method for estimating long-term ecological risks

The potential catastrophic event of a landslide bringing contaminants to surface waters has been highlighted in public media, but there are still few scientific studies analyzing the risk of landslides with contaminated soil. The aim of this study is to present a method to estimate the risk of potential long-term ecological effects on water bodies due to contaminated soil released into a river through a landslide. The study constitutes further development of previous work focusing on the instantaneous (short-term) release of contaminants and associated effects. Risk is here defined as the probability of surface water failing to comply with environmental quality standards (EQS). The transport model formulation is kept simple enough to allow for a probabilistic analysis as a first assessment of the impact on the river water quality from a landslide runout deposit containing contaminated soil. The model is applied at a contaminated site located adjacent to the Göta Älv River that discharges into the Gothenburg estuary, in southwest Sweden. The results from the case study show that a contaminated runout deposit will likely cause contamination levels above EQSs in the near area for a long time and that it will take several years for the deposit to erode, with the greatest erosion at the beginning when water velocities are their highest above the deposit. A contaminated landslide runout deposit will thus act as a source of contamination to the downstream water system until all the contaminated deposit has been eroded away and the contaminants have been transported from the deposit to the river, and further to the river mouth - diluted but not necessarily negligible. Therefore, it is important to prevent landslides of contaminated soil or waste, and if such events were to occur, to remove the contaminated runout deposit as soon as possible.

Modelling the dynamics of a large rock landslide in the Dolomites (eastern Italian Alps) using multi-temporal DEMs

Latest advances in topographic data acquisition techniques have greatly enhanced the possibility to analyse landscapes in order to understand the processes that shaped them. High-resolution Digital Elevation Models (DEMs), such as LiDAR-derived ones, provide detailed topographic information. In particular, if multi-temporal DEMs are available, it is possible to carry out a detailed geomorphic change detection analysis. This analysis may provide information about the dynamics of large landslides and may thus, be useful for landslide risk assessments. However, LiDAR-derived DEMs are mostly available only as post-event surveys. The technique is relatively recent, and local or national authorities only started widespread surveys in the last decade. Therefore, it is of a certain interest to analyse the effectiveness of DEMs derived from technical cartography to produce reliable volumetric estimates related to large landslides. This study evaluates the use of a multi-source DEM of Difference (DoD) analysis for the investigation of a large landslide –Le Laste–, which occurred on November 12, 2014 on Mount Antelao (eastern Italian Alps). The landslide initiated as a 365,000 m³ rockslide close to the summit of the mountain and transformed into a debris avalanche during its runout. The comparison of pre- and post-event DEMs allowed for the identification and quantification of erosion and deposition areas, and for the estimation of landslide volume. A sound back-analysis of the landslide with the 3D numerical model DAN3D was based on this comparison and on seismic records of the event. These seismic records proved to be remarkably useful, as they allowed for the calibration of the simulated landslide velocity. This ensured the reliability of the model notwithstanding the topographic datasets, intrinsic uncertainties. We found that using a pre-event DEM derived from technical cartography tends to slightly overestimate the volume with respect to the use of the more accurate LiDAR-derived DEM. In recent years, the landslide risk around Mt. Antelao has been increasing alongside the ever-growing population and human activities in the area. Sediment accumulations produced by the Le Laste landslide significantly amplified the debris flow hazard by providing new sediment sources. Therefore, it is crucial to delineate the distribution of this material to enable an adequate debris flow hazard assessment. The material properties derived from the back-analysis of the Le Laste landslide can be used to simulate the runout of possible future events, and to generate reliable hazard zone maps, which are necessary for effective risk mitigation.

Relationship between landslides and mountain development-Integrating geospatial statistics and a new long-term database

Development in mountainous areas is inevitable in countries with high population densities, but the actual relationship between development and landslides remains uncertain. Clarifying the key current or historical factors resulting in landslides is crucial for hazard prevention and mitigation. This study focused on the Shihmen Reservoir catchment in Taiwan. Two combinations of explanatory variables in five different years (1946, 1971, 2001, 2004, and 2012) collected from a geodatabase and digital archives were used to conduct proximity and discrete logistic regression analyses. The results demonstrate that landslides increased dramatically from 1946 to 2012 in the catchment area. The proximity and overlapping of human development with landslides increased. However, the logistic regression results indicated that variation in susceptibility to landslides was due to natural causes, with the exception of historical deforestation and newly constructed road systems. Therefore, well-recovered historical woodland sites might currently be landslide-prone areas. We suggest that cumulative historical events should be considered as explanatory variables in future landslide prediction analysis.

LAND-deFeND - An innovative database structure for landslides and floods and their consequences

Information on historical landslides and floods - collectively called "geo-hydrological hazards - is key to understand the complex dynamics of the events, to estimate the temporal and spatial frequency of damaging events, and to quantify their impact. A number of databases on geo-hydrological hazards and their consequences have been developed worldwide at different geographical and temporal scales. Of the few available database structures that can handle information on both landslides and floods some are outdated and others were not designed to store, organize, and manage information on single phenomena or on the type and monetary value of the damages and the remediation actions. Here, we present the LANDslides and Floods National Database (LAND-deFeND), a new database structure able to store, organize, and manage in a single digital structure spatial information collected from various sources with different accuracy. In designing LAND-deFeND, we defined four groups of entities, namely: nature-related, human-related, geospatial-related, and information-source-related entities that collectively can describe fully the geo-hydrological hazards and their consequences. In LAND-deFeND, the main entities are the nature-related entities, encompassing: (i) the "phenomenon", a single landslide or local inundation, (ii) the "event", which represent the ensemble of the inundations and/or landslides occurred in a conventional geographical area in a limited period, and (iii) the "trigger", which is the meteo-climatic or seismic cause (trigger) of the geo-hydrological hazards. LAND-deFeND maintains the relations between the nature-related entities and the human-related entities even where the information is missing partially. The physical model of the LAND-deFeND contains 32 tables, including nine input tables, 21 dictionary tables, and two association tables, and ten views, including specific views that make the database structure compliant with the EC INSPIRE and the Floods Directives. The LAND-deFeND database structure is open, and freely available from http://geomorphology.irpi.cnr.it/tools.

Urban construction and demolition waste and landfill failure in Shenzhen, China

On December 20, 2015 at 11:40 am a landslide in one of China's most advanced cities, Shenzhen, killed 73 people and damaged 33 buildings. In the absence of heavy rainfall or earthquakes, the landslide was an unexpected and profound shock to many people. According to China's Ministry of Land and Resources, the landslide was triggered by the collapse of an enormous pile of construction and demolition waste (CDW). With China's rapid urbanization, an increasing amount of CDW is being generated, especially in major cities. In total, China produces some 30% of the world's municipal solid waste and of this about 40% is CDW. To prevent landslides associated with CDW, the volume of waste dumped in landfills should be regulated. More specifically 4-Rs (reduce, reuse, recycle and recover) policies should be implemented more widely and efficiently. Although landfill will continue to be an important disposal option, proper management and careful monitoring of CDW are urgently needed to satisfy pressing safety issues. International collaboration, sharing of knowledge, and use of the latest technologies are needed so that the similar landslides can be prevented in China and elsewhere.

The Potential Role of Tree Diversity in Reducing Shallow Landslide Risk

Recently, interest in utilizing ecosystems for disaster risk reduction has increased, even though there remains considerable uncertainty regarding the role of ecosystems in buffering against natural hazards. This ecosystem role can be considered an ecosystem service. Although a strong body of evidence shows that biodiversity enhances ecosystem services, there are only a few studies of the relationship between biodiversity and the role of the ecosystem in reducing the risk of natural disasters. To explore the desired state of an ecosystem for disaster risk reduction we applied the finding that biodiversity enhances ecosystem services to evaluate the role of woody vegetation in reducing the frequency and severity of shallow landslides. Using information related to shallow landslides and woody vegetation in Japan as a case study, we compared the severity of shallow landslides (i.e., landslide volume) with tree species richness. Although we provide no direct evidence that tree species richness reduces shallow landslide volume, we found that the predictability of the model, which evaluated relationships between landslide volume and environmental variables in watersheds throughout the Japanese Archipelago, increased with tree species richness. This finding suggests that biodiversity is likely associated with shallow landslide risk reduction, emphasizing a possible reduction of spatial and temporal uncertainty in the roles of woody vegetation. Our study identifies a need for socioecological systems to build new approaches found on the functionality of such ecosystems.

A heuristic approach to global landslide susceptibility mapping

Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems.

Geotechnical assessment of road failure and slope monitoring along Nsukka-Adoru-Idah highway, Southeastern Nigeria

The quality of highway pavement is greatly influenced by the subgrade materials, the general geology of the area, and the materials used for construction. Investigation into the 75-km Nsukka-Adoru-Idah highway revealed that the pavement was underlain by three lithological units-Imo, Nsukka, and Ajali formations. The geotechnical evaluation carried out in the study includes the particle size distribution, Atterberg limit, specific gravity, compaction tests, and California bearing ratio (CBR). The base course has clay/silt (7-14%), fine sand (1-4%), medium sand (6-13%), and coarse sand (65-86%), while the subgrade presented clay/silt (74-82%), fine sand (6-9%), medium sand (10-17%), and coarse sand (1-3%). The average specific gravity results for the studied base course and subgrades are 2.58 and 2.52. Liquid limit (LL) result ranges from 27 to 60%, while plastic limit (PL) ranges between 17 and 24%, and plasticity index (PI) ranges from 5 to 39%. The maximum dry density (MDD) result ranges from 1.70 to 2.10 mg/m³, while the optimum moisture content (OMC) for the samples ranges between 14.1 and 18.0%. The CBR result for soaked and unsoaked samples ranges from 37 to 74 and 48 to 83%, respectively. The low unsoaked CBR (<80%) and high Atterberg limits (LL > 30% and PI > 12%) failed the stipulated Nigerian standard, signifying the need for stabilization. A geotechnical model of a highway road cut generated a factor of safety of 1.45, indicating possibility of slope failure.