Natural Hazard Susceptibility Assessment for Road Planning Using Spatial Multi-Criteria Analysis

Climate change projected to impact structural hillslope connectivity at the global scale

Structural connectivity describes how landscapes facilitate the transfer of matter and plays a critical role in the flux of water, solutes, and sediment across the Earth's surface. The strength of a landscape's connectivity is a function of climatic and tectonic processes, but the importance of these drivers is poorly understood, particularly in the context of climate change. Here, we provide global estimates of structural connectivity at the hillslope level and develop a model to describe connectivity accounting for tectonic and climate processes. We find that connectivity is primarily controlled by tectonics, with climate as a second order control. However, we show climate change is projected to alter global-scale connectivity at the end of the century (2070 to 2100) by up to 4% for increasing greenhouse gas emission scenarios. Notably, the Ganges River, the world's most populated basin, is projected to experience a large increase in connectivity. Conversely, the Amazon River and the Pacific coast of Patagonia are projected to experience the largest decreases in connectivity. Modeling suggests that, as the climate warms, it could lead to increased erosion in source areas, while decreased rainfall may hinder sediment flow downstream, affecting landscape connectivity with implications for human and environmental health.

Evaluation and Mitigation of Flash Flood Risks in Arid Regions: A Case Study of Wadi Sudr in Egypt

Flash floods threaten the lives of people and properties in different regions around the world, especially in arid and semi-arid regions due to infrequent flood events. The current study aims to assess the geomorphological parameters of Wadi Sudr, South Sinai in Egypt to evaluate flash flood risks and provide adequate mitigation methods. This study presents an integrated method that combines geographic information system (GIS) and watershed modeling system (WMS) with HEC-HMS to visualize and assess flood events in the study area. Different morphologic parameters of the watershed were determined, including linear, areal, and relief parameters. GIS was used to analyze the satellite images and determine the characteristics of the valley to get the extension and number of stream orders in the valley, then WMS was used to estimate rainstorms and basin characteristics, as well as estimate the amount of rain that causes flooding. HEC-HMS program was used for hydrological demonstration and precipitation overflow estimation. The morphometric analysis provided a quantitative portrayal of the Wadi Sudr watershed. Wadi Sudr has 4029 streams connected with seventh order of streams spread over an area of 547.45 km2. Based on the results of morphologic and hydraulic parameters of the watershed, two locations of protection dams were suggested. A comparison between the two locations was made to select the best location based on some criteria, including storage capacity, water depth behind the dam, width and shape of the valley, and the area covered by water stored in the reservoir. The comparison between the two locations showed that the first location is more appropriate for dam construction based on the examined criteria. The valley shape in the first location is more regular than in the second. The first location provided higher storage capacity and water depth in front of the dam than the second. The area covered by water and the width of the valley is less than the second. The stability of the dam at the first site could be higher and the cost of construction could cost be less than the second due to these reasons. A comparison was made using the weighted linear combination (WLC) method, which consists of 13 criteria to determine the suitability index (SI) in order to select the best location from the proposed locations. SI proved that the first location is better than the second. The designed dam in the selected site could be cost-efficient to protect the study area from flood risks and harvesting water that can be used in different purposes. This methodology can be applied in different areas for mitigating flash flood risks.

Advanced machine learning algorithms for flood susceptibility modeling - performance comparison: Red Sea, Egypt

Floods are among the most devastating environmental hazards that directly and indirectly affect people's lives and activities. In many countries, sustainable environmental management requires the assessment of floods and the likely flood-prone areas to avoid potential hazards. In this study, the performance and capabilities of seven machine learning algorithms (MLAs) for flood susceptibility mapping were tested, evaluated, and compared. These MLAs, including support vector machine (SVM), random forest (RF), multivariate adaptive regression spline (MARS), boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA), were tested for the area between Safaga and Ras Gharib cities, Red Sea, Egypt. A geospatial database was developed with eleven flood-related factors, namely altitude, slope aspect, lithology, land use/land cover (LULC), slope length (LS), topographic wetness index (TWI), slope angle, profile curvature, plan curvature, stream power index (SPI), and hydrolithology units. In addition, 420 actual flooded areas were recorded from the study area to create a flood inventory map. The inventory data were randomly divided into training group with 70% and validation group with 30%. The flood-related factors were tested with a multicollinearity test, the variance inflation factor (VIF) was less than 2.135, the tolerance (TOL) was more than 0.468, and their importance was evaluated with a partial least squares (PLS) method. The results show that RF performed the best with the highest AUC (area under curve) value of 0.813, followed by GLM with 0.802, MARS with 0.801, BRT with 0.777, MDA with 0.768%, FDA with 0.763, and SVM with 0.733. The results of this study and the flood susceptibility maps could be useful for environmental mitigation, future development activities in the area, and flood control areas.

A methodological proposal to evaluate the health hazard scenario from COVID-19 in Italy

2019 Coronavirus disease (COVID-19) had a big impact in Italy, mainly concentrated in the northern part of the Country. All this was mainly due to similarities of this area with Wuhan in Hubei Province, according to geographical, environmental and socio-economic points of view. The basic hypothesis of this research was that the presence of atmospheric pollutants can generate stress on health conditions of the population and determine pre-conditions for the development of diseases of the respiratory system and complications related to them. In most cases the attention on environmental aspects is mainly concentrated on pollution, neglecting issues such as land management which, in some way, can contribute to reducing the impact of pollution. The reduction of land take and the decrease in the loss of ecosystem services can represent an important aspect in improving environmental quality. In order to integrate policies for environmental change and human health, the main factors analyzed in this paper can be summarized in environmental, climatic and land management. The main aim of this paper was to produce three different hazard scenarios respectively related to environmental, climatic and land management-related factors. A Spatial Analytical Hierarchy Process (AHP) method has been applied over thirteen informative layers grouped in aggregation classes of environmental, climatic and land management. The results of the health hazard maps show a disparity in the distribution of territorial responses to the pandemic in Italy. The environmental components play an extremely relevant role in the definition of the red zones of hazard, with a consequent urgent need to renew sustainable development strategies. The comparison of hazard maps related to different scenarios provides decision makers with tools to orient policy choices with a different degree of priority according to a place-based approach. In particular, the geospatial representation of risks could be a tool for legitimizing the measures chosen by decision-makers, proposing a renewed approach that highlights and takes account of the differences between the spatial contexts to be considered - Regions, Provinces, Municipalities - also in terms of climatic and environmental variables.

An Integrated Methodology Using Extended Swara and Dea for the Performance Analysis of Wastewater Treatment Plants: Turkey Case

Public and private companies make significant water infrastructure investments to meet increasing water demand. In this context, investments in wastewater treatment plants (WWTPs), which play an important role in recycling of used water, are also increasing. This study investigates determination of the efficiency scores of WWTPs considering each metropolitan municipality as a decision-making unit (DMU). In this study, a two-step methodology is established to determine efficiency scores of WWTPs. In the first step, the input and output parameters are searched by a literature review for the performance evaluation, and candidate parameters are determined. Then, to determine the most appropriate and related parameters, the importance weights of all candidate inputs and outputs are computed using the extended stepwise weight assessment ratio analysis (SWARA) method. Next, the inputs and outputs are chosen according to their importance weights. In the second step, efficiency scores of WWTPs are calculated using output-oriented data envelopment analysis (DEA) models. Based on the expert opinions, the parameters used as input variables are as follows: Daily Wastewater Amount per Person Discharged in Municipalities, WWTP Capacity, and Number of WWTPs; and the parameters used as output variables are as follows; Amount of Wastewater Treated in WWTPs and Municipal Population Served by WWTPs. The results are presented and discussed by sensitivity analysis. Results show that 14 metropolitan municipalities have total efficiency, 19 metropolitan municipalities have technical efficiency, and 21 metropolitan municipalities have scale efficiency.

Integrated machine learning methods with resampling algorithms for flood susceptibility prediction

Flood susceptibility projections relying on standalone models, with one-time train-test data splitting for model calibration, yields biased results. This study proposed novel integrative flood susceptibility prediction models based on multi-time resampling approaches, random subsampling (RS) and bootstrapping (BT) algorithms, integrated with machine learning models: generalized additive model (GAM), boosted regression tree (BTR) and multivariate adaptive regression splines (MARS). RS and BT algorithms provided 10 runs of data resampling for learning and validation of the models. Then the mean of 10 runs of predictions is used to produce the flood susceptibility maps (FSM). This methodology was applied to Ardabil Province on coastal margins of the Caspian Sea which faced destructive floods. The area under curve (AUC) of receiver operating characteristic (ROC) and true skill statistic (TSS) and correlation coefficient (COR) were utilized to evaluate the predictive accuracy of the proposed models. Results demonstrated that resampling algorithms improved the performance of Standalone GAM, MARS and BRT models. Results also revealed that Standalone models had better performance with the BT algorithm compared to the RS algorithm. BT-GAM model attained superior performance in terms of statistical measures (AUC = 0.98, TSS = 0.93, COR = 0.91), followed by BT-MARS (AUC = 0.97, TSS = 0.91, COR = 0.91) and BT-BRT model (AUC = 0.95, TSS = 0.79, COR = 0.79). Results demonstrated that the proposed models outperformed the benchmark models such as Standalone GAM, MARS, BRT, multilayer perceptron (MLP) and support vector machine (SVM). Given the admirable performance of the proposed models in a large scale area, the promising results can be expected from these models for other regions.

Methodological proposal for the allocation of water quality monitoring stations using strategic decision analysis

In order to fill a gap in the monitoring of water quality in Brazil, the objective of this study was to propose a methodology to support the allocation of water quality monitoring stations in river basins. To achieve this goal, eight criteria were selected and weighted according to their degree of importance. It was taken into account the opinion of water resources management experts. In addition, a decision support system was designed so that the methodology could be used in the allocation of water quality monitoring stations by researchers and management bodies of water resources, to be fully implemented in geographic information system environment. In order to demonstrate the potential of the proposed methodology, which can be used in places that have or not existing monitoring networks, it has been applied in the Minas Gerais portion of the Doce river basin. Because the area already has a monitoring network with 65 stations in operation under the responsibility of the Minas Gerais Water Management Institute (IGAM), an expansion of the network was suggested and a simulation of a scenario was performed considering that the study area did not have an established network. The results of the analyses consisted of maps of suitability, indicating the locations with greater and lesser suitability for the establishment of the stations. With the application of the methodology, seven new sites were proposed so that the study area had the density recommended by the National Water Agency (ANA), and it was verified that the Caratinga River Water Resources Management Unit (UGRH5 Caratinga) has the most deficiency of stations among the six units evaluated in the Minas Gerais portion of the Doce river basin. In the simulated scenario considering the non-existence of a network, the adequacy map obtained was compared with the existing monitoring network and it was possible to classify the stations according to the purpose for which they were established, such as monitoring environments under anthropic activities or establishing benchmarks for the water bodies. Overall, the proposed methodology proved itself robust, and although the results were specific to one basin, the criteria and decision support system used are fully applicable to other areas of study.

GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

Check dams are widely used watershed management measures for reducing flood peak discharge and sediment transport, and increasing lag time and groundwater recharge throughout the world. However, identifying the best suitable sites for check dams within the stream networks of various watersheds remains challenging. This study aimed to develop an open-source software with user-friendly interface for screening the stream network possibilities and identifying and guiding the selection of suitable sites for check dams within watersheds. In this developed site selection software (SSS), multi-criteria decision analysis (MCDA) was integrated into geographic information systems (GIS), which allowed for numerous spatial data of the multiple criteria to be relatively simply and visually processed. Different geomorphometric and topo-hydrological factors were considered and accounted for to enhance the SSS identification of the best locations for check dams. The factors included topographic wetness index (TWI), terrain ruggedness index (TRI), topographic position index (TPI), sediment transport index (STI), stream power index (SPI), slope, drainage density (DD), and stream order (SO). The site identification performance of the SSS was assessed using the receiver operating characteristic (ROC) curve method, with results for the case study example of the Poldokhtar watershed in Iran showing excellent performance and identifying 327 potential sites for efficient check dam construction in this watershed. The SSS tool is not site-specific but is rather general, adaptive, and comprehensive, such that it can and should be further applied and tested across different watersheds and parts of the world.

Meeting sustainable development challenges in growing cities: Coupled social-ecological systems modeling of land use and water changes

Ongoing urban expansion may degrade natural resources, ecosystems, and the services they provide to human societies, e.g., through land use and water changes and feedbacks. In order to control and minimize such negative impacts of urbanization, best practices for sustainable urban development must be identified, supported, and reinforced. To accomplish this, assessment methods and tools need to consider the couplings and feedbacks between social and ecological systems, as the basis for improving the planning and management of urban development. Collaborative efforts by academics, urban planners, and other relevant actors are also essential in this context. This will require relevant methods and tools for testing and projecting scenarios of coupled social-ecological system (CSES) behavior, changes, and feedbacks, in support of sustainable development of growing cities. This paper presents a CSES modeling approach that can provide such support, by coupling socio-economically driven land use changes and associated hydrological changes. The paper exemplifies and tests the applicability of this approach for a concrete case study with relevant data availability, the Tyresån catchment in Stockholm County, Sweden. Results show that model integration in the approach can reveal impacts of urbanization on hydrological and water resource, and the implications and feedbacks for urban societies and ecosystems. The CSES approach introduces new model challenges, but holds promise for improved model support towards sustainable urban development.

Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models—support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards.

A Method for Urban Flood Risk Assessment and Zoning Considering Road Environments and Terrain

Floods have been severely threatening social development worldwide. The occurrence of floods has multiple factors, and the flood risk considering road environments needs comprehensive analysis from meteorology, underlying surface, and urban road network. Thus, this study proposes an integrated method and constructs a road risk zoning model (RRZM). In the RRZM, submerged depth was obtained by the Soil Conservation Service (SCS) model, and the degree of road importance was obtained by the analytical hierarchy process (AHP) method. These two parts were used to characterize road vulnerability. Then the flood risk grade was evaluated based on the optimized artificial neural network (ANN). Finally, the results of flood risk assessment were obtained by road vulnerability and flood risk grade. The RRZM was applied to the Chang-Zhu-Tan Urban Agglomeration (CZTUA), China. The results showed that the spatial distributions of flood risk and the extent of road damage varied remarkably in different cities. Changsha was the most sensitive city to floods in the CZTUA. The flood risk zones were classified into six levels, and the vulnerable road sections identified from the risk zones at level 6 in the maps carried more traffic volume than others. By comparing with existing methods, it was found that the RRZM effectively reflected the spatial characteristics of flood risk considering road environments. It provides a new perspective for urban flood risk assessment and disaster response decision-making.

Assessing flood probability for transportation infrastructure based on catchment characteristics, sediment connectivity and remotely sensed soil moisture

Flooding may damage important transportation infrastructures, such as roads, railways and bridges, which need to be well planned and designed to be able to withstand current and possible future climate-driven increases in flood frequencies and magnitudes. This study develops a novel approach to predictive statistical modelling of the probability of flooding at major road-stream intersection sites, where water, sediment and debris can accumulate and cause failure of drainage facilities and associated road damages. Two areas in south-west Sweden, affected by severe floods in August 2014, are used in representative case studies for this development. A set of physical catchment-descriptors (PCDs), characterizing key aspects of topography, morphology, soil type, land use, hydrology (precipitation and soil moisture) and sediment connectivity in the water- and sediment-contributing catchments, are used for the predictive flood modelling. A main novel contribution to such modelling is to integrate the spatiotemporal characteristics of remotely-sensed soil moisture in indices of sediment connectivity (IC), thereby also allowing for investigation of the role of soil moisture in the flood probability for different road-stream intersections. The results suggest five categories of PCDs as especially important for flood probability quantification and identification of particularly flood-prone intersections along roads (railways, etc.) These include: channel slope at the road-stream intersection and average elevation, soil properties (mainly percentage of till), land use cover (mainly percentage of urban areas), and a sediment connectivity index that considers soil moisture in addition to morphology over the catchment.

A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran

In north of Iran, flood is one of the most important natural hazards that annually inflict great economic damages on humankind infrastructures and natural ecosystems. The Kiasar watershed is known as one of the critical areas in north of Iran, due to numerous floods and waste of water and soil resources, as well as related economic and ecological losses. However, a comprehensive and systematic research to identify flood-prone areas, which may help to establish management and conservation measures, has not been carried out yet. Therefore, this study tested four methods: evidential belief function (EBF), frequency ratio (FR), Technique for Order Preference by Similarity To ideal Solution (TOPSIS) and Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) for flood hazard susceptibility mapping (FHSM) in this area. These were combined in two methodological frameworks involving statistical and multi-criteria decision making approaches. The efficiency of statistical and multi-criteria methods in FHSM were compared by using area under receiver operating characteristic (AUROC) curve, seed cell area index and frequency ratio. A database containing flood inventory maps and flood-related conditioning factors was established for this watershed. The flood inventory maps produced included 132 flood conditions, which were randomly classified into two groups, for training (70%) and validation (30%). Analytical hierarchy process (AHP) indicated that slope, distance to stream and land use/land cover are of key importance in flood occurrence in the study catchment. In validation results, the EBF model had a better prediction rate (0.987) and success rate (0.946) than FR, TOPSIS and VIKOR (prediction rate 0.917, 0.888, and 0.810; success rate 0.939, 0.904, and 0.735, respectively). Based on their frequency ratio and seed cell area index values, all models except VIKOR showed acceptable accuracy of classification.

Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS

Every year, gully erosion causes substantial damage to agricultural land, residential areas and infrastructure, such as roads. Gully erosion assessment and mapping can facilitate decision making in environmental management and soil conservation. Thus, this research aims to propose a new model by combining the geographically weighted regression (GWR) technique with the certainty factor (CF) and random forest (RF) models to produce gully erosion zonation mapping. The proposed model was implemented in the Mahabia watershed of Iran, which is highly sensitive to gully erosion. Firstly, dependent and independent variables, including a gully erosion inventory map (GEIM) and gully-related causal factors (GRCFs), were prepared using several data sources. Secondly, the GEIM was randomly divided into two groups: training (70%) and validation (30%) datasets. Thirdly, tolerance and variance inflation factor indicators were used for multicollinearity analysis. The results of the analysis corroborated that no collinearity exists amongst GRCFs. A total of 12 topographic, hydrologic, geologic, climatologic, environmental and soil-related GRCFs and 150 gully locations were used for modelling. The watershed was divided into eight homogeneous units because the importance level of the parameters in different parts of the watershed is not the same. For this purpose, coefficients of elevation, distance to stream and distance to road parameters were used. These coefficients were obtained by extracting bi-square kernel and AIC via the GWR method. Subsequently, the RF-CF integrated model was applied in each unit. Finally, with the units combined, the final gully erosion susceptibility map was obtained. On the basis of the RF model, distance to stream, distance to road and land use/land cover exhibited a high influence on gully formation. Validation results using area under curve indicated that new GWRCFRF approach has a higher predictive accuracy 0.967 (96.7%) than the individual models of CF 0.763 (76.3%) and RF 0.776 (77.6%) and the CF-RF integrated model 0.897 (89.7%). Thus, the results of this research can be used by local managers and planners for environmental management.

An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines

Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human life. Modeling flood susceptibility in watersheds and reducing the damages caused by flooding is an important component of environmental and water management. The current study employs two new algorithms for the first time in flood susceptibility analysis, namely multivariate discriminant analysis (MDA), and classification and regression trees (CART), incorporated with a widely used algorithm, the support vector machine (SVM), to create a flood susceptibility map using an ensemble modeling approach. A flood susceptibility map was developed using these models along with a flood inventory map and flood conditioning factors (including altitude, slope, aspect, curvature, distance from river, topographic wetness index, drainage density, soil depth, soil hydrological groups, land use, and lithology). The case study area was the Khiyav-Chai watershed in Iran. To ensure a more accurate ensemble model, this study proposed a framework for flood susceptibility assessment where only those models with an accuracy of >80% were permissible for use in ensemble modeling. The relative importance of factors was determined using the Jackknife test. Results indicated that the MDA model had the highest predictive accuracy (89%), followed by the SVM (88%) and CART (0.83%) models. Sensitivity analysis showed that slope percent, drainage density, and distance from river were the most important factors in flood susceptibility mapping. The ensemble modeling approach indicated that residential areas at the outlet of the watershed were very susceptible to flooding, and that these areas should, therefore, be prioritized for the prevention and remediation of floods.