Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future

Climate warming modifies hydrological responses in the southern Western Ghats and the western coastal plains (India): Insights from CMIP6-VIC simulations

Recent changes in global precipitation patterns have led to unparalleled floods, landslides, and droughts, significantly impacting lives, infrastructure, and the environment. Understanding regional-scale climate change impacts on hydrological responses has attracted researchers, primarily using climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This study focuses on the southern Western Ghats (SWG) and western coastal plains (WCP) of the Indian subcontinent to assess climate change impacts on regional hydrology. By analyzing climate data from nine CMIP6 General Circulation Models (GCMs) and simulating hydrological fluxes with the Variable Infiltration Capacity (VIC) model, we demonstrate that all models robustly project an intensification of the hydrological cycle due to climate warming under two shared socioeconomic pathways (SSPs) - SSP245 and SSP585 - especially during the Indian Summer Monsoon (ISM) season. The region is projected to experience up to a 20 % increase in annual precipitation per 1 °C rise in temperature, with extreme precipitation events (EPEs) potentially increasing by up to 16 % per degree of warming. Future runoff is expected to rise under both SSP scenarios, with watersheds experiencing markedly wetter ISM season and drier pre-monsoon (PRM) season compared to the baseline period, leading to risks associated with concurrent floods/landslides and droughts. These changes underscore the necessity for region-specific adaptation strategies to manage water resources effectively. Adaptation strategies to enhance short-term water storage during the ISM season could partially mitigate water shortages during the PRM season, offering potential benefits in the wake of climate change. Our findings highlight the need for integrated water resources management plans to address the challenges posed by climate change, ensuring sustainable water availability, agricultural productivity, and hydropower generation.

Geospatial assessment of environmental factors and flooding occurrences in Borno Metropolis, Northeastern Nigeria (1987-2024)

Climate change, driven by human and natural processes, has increased flood frequency, impacting infrastructure, and resources. This study explores the relationship between land use/land cover (LULC) changes, rainfall patterns, and floods in Borno Metropolis, Nigeria, during the 2024 floods. Using Google Earth Engine (GEE), Landsat images from 1987 to 1990, 2013 to 2014, and 2024 were analyzed to calculate environmental indices, including the soil adjusted vegetation index (SAVI), normalized difference water index (NDWI), and normalized difference built-up index (NDBI). Sentinel-1 Synthetic Aperture Radar (SAR) images identified flood-affected areas in 2024. Rainfall data from CHIRPS (1987-2024) were analyzed using Mann-Kendall and Sen's slope tests. Rule-based classification identified environmental changes, and statistical tests such as Pearson, Spearman, Kendall, and point-biserial were applied to assess relationships between climatic and environmental factors and floods. Python was used for all analyses. The findings revealed that 330 km2 (12.6%) of the total area experienced flooding in 2024. Vegetation cover decreased by 16.1 km2 (0.61%) in 2024 compared to 1987-1990, and non-vegetated areas increased significantly, reaching 19.5 km2 in 2024. Built-up/bareland areas expanded by 59.4 km2 (2.39%) from 2013-2014 to 2024. Spearman analysis effectively highlighted non-linear relationships between indices and floods. Point-biserial tests confirmed correlations between rainfall and flooding( r pb = 0.15 , p = < 0.001 ) , indicating that higher rainfall levels increase flood likelihood. The heavy rainfall of 863 mm in 2024 was a key factor in increasing runoff and intensifying floods. This study highlights critical flood-affected areas, providing valuable insights for flood management planning to help governments and local communities reduce risks.

Estimation of Flood Inundation Area Using Soil Moisture Active Passive Fractional Water Data with an LSTM Model

Accurate flood monitoring and forecasting techniques are important and continue to be developed for improved disaster preparedness and mitigation. Flood estimation using satellite observations with deep learning algorithms is effective in detecting flood patterns and environmental relationships that may be overlooked by conventional methods. Soil Moisture Active Passive (SMAP) fractional water (FW) was used as a reference to estimate flood areas in a long short-term memory (LSTM) model using a combination of soil moisture information, rainfall forecasts, and floodplain topography. To perform flood modeling in LSTM, datasets with different spatial resolutions were resampled to 30 m spatial resolution using bicubic interpolation. The model's efficacy was quantified by validating the LSTM-based flood inundation area with a water mask from Senti-nel-1 SAR images for regions with different topographic characteristics. The average area under the curve (AUC) value of the LSTM model was 0.93, indicating a high accuracy estimation of FW. The confusion matrix-derived metrics were used to validate the flood inundation area and had a high-performance accuracy of ~0.9. SMAP FW showed optimal performance in low-covered vegetation, seasonal water variations and flat regions. The estimates of flood inundation areas show the methodological promise of the proposed framework for improved disaster preparedness and resilience.

Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system

The escalating impacts of climate change have intensified extreme rainfall events, placing urban drainage systems under unprecedented pressure and increasing flood risks. Addressing these challenges requires advanced flood mitigation strategies, optimized sewer operations, and responsive disaster management. This study leverages knowledge graphs to integrate diverse data sources, providing a comprehensive perspective on flood dynamics, and applies deep learning models within a Real-Time Urban Drainage Early Warning System to enhance flood management at Taipei City's Zhongshan Pumping Station in Taiwan. We proposed deep learning models, specifically Convolutional Neural Networks combined with Back Propagation Neural Networks (CNN-BP), to make multi-input multi-output multi-step (MIMOMS) forecasts on sewer water levels at intervals from 10 to 40 min (T+1 to T+4) and MIMO forecasts on the pumping station's internal (forebay) and external (river) water levels at intervals from 10 to 60 min (T+1 to T+6). The CNN-BP model exhibited superior forecast accuracy, reaching an R2 (RMSE) of 0.97 (0.08m) at T+1 for sewer water levels and an R2 (RMSE) of 0.99 (0.06m) at T+1 for both internal and external water levels. These results highlight CNN-BP's capability to accurately capture water level trends, ensuring reliable real-time responsiveness, especially during intense and sudden rainfall events. The CNN-BP's high predictive accuracy enables enhanced pump operations, strengthens early warning systems, and fosters intelligent flood control practices crucial for effective environmental management.

The cost of flooding on housing under climate change in the Philippines: Examining projected damage at the local scale

While the Philippines has made significant strides in proactive disaster risk reduction measures, current planning actions are undertaken primarily based on historical flood risk. There are gaps in understanding how the escalating impacts of climate change will alter flood dynamics. This study examines shifting local flood risk patterns in the Municipality of Carigara in Leyte. We quantify probabilistic flood damage on residential structures for early, mid-, and late-term flood scenarios under RCP4.5 and RCP8.5 pathways. By utilising localised housing vulnerability functions, we assess risk trends at a household level, considering concrete, light material, and elevated light material housing typologies. Our results indicate a 3 % decrease in future flood damages to residential structures under RCP 4.5 and a 34 % decrease in damages under RCP 8.5 by 2100 attributable to climate change for 100-year flood events. These shifts highlight the nuances of regional changes in flood damages over the next century. The findings provide insights into how localised climate-risk assessments for municipalities might be established as entry points to inform climate change policies and projects. Through established mechanisms such as Local Disaster Risk Reduction Management Funds (LDRRMF) in the Philippines, we propose methods of climate-informed decision-making for local government units to minimise damage for future climate scenarios.

Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review

This systematic review examines the application of artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for climate change adaptation and mitigation in Iran, Pakistan, and Turkey. These three nations-key Economic Cooperation Organization (ECO) members and a nexus between Europe and South Asia-are experiencing diverse environmental challenges due to varying climatic conditions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search in the Scopus database, ultimately identifying 76 relevant articles out of an initial 492. Although some articles utilized multiple techniques, classical ML methods appeared in approximately 37.3 % of the studies, neural network paradigms in about 57.5 %, and optimization or meta-heuristic algorithms in around 5.0 %. Regarding thematic focus, about 33.3 % of the articles addressed water resource management, 22.2 % focused on climate prediction, 11.1 % on land and agriculture, 9 % on ecosystem modeling, and 24.2 % on natural disaster preparedness and response. The analysis reveals a growing but uneven body of research utilizing AI across the ECO countries. By highlighting successful applications, identifying key gaps-such as limited cross-border collaboration and inconsistent data availability-and proposing a framework for more integrated research, this review aims to guide future initiatives that leverage AI's potential to improve climate resilience and sustainability in the region.

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan-under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041-2060 and 2081-2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region.

Multi-hazard susceptibility model based on high spatial resolution data-a case study of Sali settlement (Dugi otok, Croatia)

The world has been facing an increase in various natural hazards. The coastal regions are recognized as one of the most vulnerable due to high population pressure and climate change intensity. Mediterranean countries have one of the most burnable ecosystems in the world, one of the most exposed to pluvial floods, and have the highest erosion rates within the EU. Therefore, the aim of this study was to develop the first multihazard susceptibility model in Croatia for the Sali settlement (island of Dugi otok). The creation of a multi-hazard susceptibility model (MHSM) combined the application of geospatial technology (GST) with a local perception survey. The methodology consisted of two main steps: (1) creating individual hazard susceptibility models (soil erosion, wildfires, pluvial floods), and (2) overall hazard susceptibility modeling. Multicriterial GIS analyses and the Analytical Hierarchy Process were used to create individual hazard models. Criteria used (32) to create models are derived from very-high-resolution (VHR) models. Two versions of MHSM are created: 1) all criteria with equal weighting coefficients and 2) weight coefficients determined based on public perception. According to MHSM 1, most of the research (58%) area is moderately susceptible to multiple hazards. Highly and very highly susceptible areas are 27% of the drainage basin and are mostly located near roads and houses. MHSM 2 reveals similar results to MHSM 1. The public perceives that the research area is the most susceptible to wildfires. The wildfire ignition risk is ranked as moderate (3.00) with a standard deviation of 1.16. Pluvial flood risk is ranked low (2.78), with a standard deviation of 1.15. The risk of soil is most inferior (2.24) with a standard deviation of 0.91. The the most significant difference between public perception and the GIS-MCDA model of hazard susceptibility is related to soil erosion. However, the accuracy of the soil erosion model was confirmed by ROC curves based on recent traces of soil erosion in the research area. The proposed methodological framework of multi-hazard susceptibility modeling can be applied, with minor modifications, to other Mediterranean countries.

Assessment of machine learning algorithms and new hybrid multi-criteria analysis for flood hazard and mapping

Floods in Iran cause a lot of damage in different places every year. The 2019 floods of the Gorgan and Atrak rivers basins in the north of Iran were one of the most destructive events in this country. Therefore, investigating the flood hazard of these areas is very necessary to manage probable future floods. For this purpose, in the present study, the capability of Random Forest (RF) and Support Vector Machine (SVM) algorithms was investigated in combination with Sentinel series and Landsat-8 images to prepare the 2019 flood map. Then, the flood hazard map of these areas was prepared using the new hybrid Fuzzy Best Worse Model-Weighted Multi-Criteria Analysis (FBWM-WMCA) model. According to the results of the FBWM-WMCA model, 38.58%, 50.18%, 11.10%, and 0.14% of the Gorgan river basin and 45.11%, 49.96%, 4.17%, and 0.076% of the Atrak river basin are in high, medium, low, and no hazards, respectively. The highest flood hazard areas in Gorgan and Atrak rivers basins in the north, northwest, west, and east, and south and southwest are mostly at medium flood hazard. Also, the results of RF and SVM algorithms with an overall accuracy of more than 85% for Sentinel-1, Sentinel-2, and Landsat-8 images and 80% for Sentinel-3 images indicate that the flooding is related to the western, southwestern, and northern regions including agricultural, bare lands and built up. According to the obtained results and the efficiency of the FBWM-WMCA model, the Gorgan and Atrak rivers basins need proper planning for flood hazard management.

Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm

Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive Machine Learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 percent was between low and very low flood-prone zones. The models applied performed well with ROC-AUC scores greater than 70 percent and MAE, MSE, and RMSE scores less than 30 percent. DT and RF algorithms were suggested for places with similar physical characteristics based on their outstanding performance and the training datasets. The study provides valuable insights to policymakers, administrative authorities, and local stakeholders to cope with floods and enhance flood prevention measures as a climate change adaptation strategy in the AUW.

Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm

Droughts pose a severe environmental risk in countries that rely heavily on agriculture, resulting in heightened levels of concern regarding food security and livelihood enhancement. Bangladesh is highly susceptible to environmental hazards, with droughts further exacerbating the precarious situation for its 170 million inhabitants. Therefore, we are endeavouring to highlight the identification of the relative importance of climatic attributes and the estimation of the seasonal intensity and frequency of droughts in Bangladesh. With a period of forty years (1981-2020) of weather data, sophisticated machine learning (ML) methods were employed to classify 35 agroclimatic regions into dry or wet conditions using nine weather parameters, as determined by the Standardized Precipitation Evapotranspiration Index (SPEI). Out of 24 ML algorithms, the four best ML methods, ranger, bagEarth, support vector machine, and random forest (RF) have been identified for the prediction of multi-scale drought indices. The RF classifier and the Boruta algorithms shows that water balance, precipitation, maximum and minimum temperature have a higher influence on drought intensity and occurrence across Bangladesh. The trend of spatio-temporal analysis indicates, drought intensity has decreased over time, but return time has increased. There was significant variation in changing the spatial nature of drought intensity. Spatially, the drought intensity shifted from the northern to central and southern zones of Bangladesh, which had an adverse impact on crop production and the livelihood of rural and urban households. So, this precise study has important implications for the understanding of drought prediction and how to best mitigate its impacts. Additionally, the study emphasizes the need for better collaboration between relevant stakeholders, such as policymakers, researchers, communities, and local actors, to develop effective adaptation strategies and increase monitoring of weather conditions for the meticulous management of droughts in Bangladesh.

Is the existing methods sustainable? A hybrid approach to flood risk mapping

The hydraulic and integrated modeling approaches appear to stand out in the sequence of flood risk models that have been presented because of their predictive accuracy. The former has a high probability of under predicting and the latter has a high tendency to over-predict. This study proposed a methodological approach that combines the hydraulic and integrated models using analytical hierarchical raster fusion techniques to strengthen the weaknesses of the individual models. This study seeks to undertake a flood inundation model, a runoff model, and raster fusion models using GIS and HEC-RAS rain-on-grid methods to map flood risk in the Ona river basin of Ibadan city. •A hydraulic model was used to identify flood depth and inundation areas along a major stream channel, which was then extracted, rasterized, resampled, and reclassified to a spatial resolution of 5 m.•Several raster datasets (indicators) were created from land use, elevation, soil, and geological data layers using advanced GIS techniques.•AHP assisted raster data fusion model was used to combine all of the raster indicators into a single consolidated hybrid flood raster layer that revealed flood risk areas by magnitude.

Development of geo-environmental factors controlled flash flood hazard map for emergency relief operation in complex hydro-geomorphic environment of tropical river, India

The occurrences of flash floods in sub-tropical climatic regions like India are ubiquitous phenomena, particularly during the monsoon season. This type of flood occurs within a short period of time and makes it distinctive from all-natural hazards, which causes huge loss of economy and causalities of life. Therefore, its prediction is crucial and one of the challenging tasks for researchers to mitigate this sustainably. Furthermore, identifying flash flood susceptible regions is the foremost responsibility in managing flood events, which helps the local administration take emergency relief operations in flood-prone regions. In September 2021, the flood in the Gandheswari river basin was the most severe compared to the past decade. The occurrences of flash floods in the lower course of the Gandheswari river has been affected riparian habitats rigorously. Thus, in this study, we proposed the bivariate logistic regression (LR) method to delineate this river basin's flash flood hazard (FFH) map. Here, sixteen flood conditioning factors were selected for modeling purposes with the help of a multicollinearity test, and a total of 71 flood points were identified from the historical dataset. The produced result was validated by six distinctive validating techniques, including receiver operating characteristics (ROC) analysis, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and F-score. These techniques have shown that present modeling has high predictive performance in both training and testing dataset with the values of ROC (training-0.928, validating-0.892), specificity (training-0.911, validating-0.882), sensitivity (training-0.915, validating-0.885), PPV (training-0.912, validating-0.874), NPV (training-0.91, validating-0.875), and F-score (training-0.92, validating-0.89). Therefore, the proposed method in this and the outcome result will help the disaster manager make proper decisions to mitigate the hazardous situation and take sustainable emergency relief operations.

Prediction of glaciated area fraction over the Sikkim Himalayan Region, India: a comparative study using response surface method, random forest, and artificial neural network

Glacier area fraction at high altitude mountains is a serious worry in today's time triggered by climate change. The current information on this natural resource is very important for the survival of humanity as it affects the water, food, and energy security of people dependent on it. Due to its problematic accessibility and tough environmental condition, ground monitoring is quite challenging. This study investigates the impact of environmental parameters and pollutants on glacier area fraction over the Eastern Himalaya region and its prediction through random forest (RF), multilayer perceptron (MLP), radial basis function analysis (RBFN), and response surface methodology (RSM) models. The data are obtained from the Goddard Earth Sciences Data and Information Services Center (GES DISC), NASA's data archive portal ( https://giovanni.gsfc.nasa.gov ). The collinearity of independent variables reveals that all selected input parameters are highly correlated with R2 value > 0.9. The RSM and RF model provided valuable insight of the predictor's significance in addition to their capability to predict the response. The model performance was evaluated in terms of R2 value and the error matrices. The model's R2 value was found to be 0.843, 0.839, 0.838, and 0.743 for MLP, RBFN, RF, and RSM respectively. Although, the neural network model R2 values are the highest, but the most reliable and suitable model is RF as the error matrices for this model are much lower than others. This study encourages the investigation of the hybridization of these models for more accurate prediction.

Spatial and temporal patterns of indicators of climate change and variability in the Arab world in the past four decades

A comprehensive assessment of the spatial and temporal patterns of the most common indicators of climate change and variability in the Arab world in the past four decades was carried out. Monthly maximum and minimum air temperature and precipitation amount data for the period 1980-2018 were obtained from the CHELSA project with a resolution of 1 km2, which is suitable for detecting local geographic variations in climatic patterns. This data was analyzed using a seasonal-Kendall metric, followed by Sen's slope analysis. The findings indicate that almost all areas of the Arab world are getting hotter. Maximum air temperatures increased by magnitudes varying from 0.027 to 0.714 °C/decade with a mean of 0.318 °C/decade while minimum air temperatures increased by magnitudes varying from 0.030 to 0.800 °C/decade with a mean of 0.356 °C/decade. Most of the Arab world did not exhibit clear increasing or decreasing precipitation trends. The remaining areas showed either decreasing or increasing precipitation trends. Decreasing trends varied from -0.001 to -1.825 kg m-2/decade with a mean of -0.163 kg m-2/decade, while increasing trends varied from 0.001 to 4.286 kg m-2/decade with a mean of 0.366 kg m-2/decade. We also analyzed country-wise data and identified areas of most vulnerability in the Arab world.

Non-stationary evaluation of runoff peaks in response to climate variability and land use change in Ferson Creek, Illinois, USA

In this paper, we examine how surface runoff affects public safety and urban infrastructure worldwide and how human activity has significantly altered the frequency and magnitude of these events. We investigate this issue in Ferson Creek, IL, USA. Our study focuses on three specific areas of impact: (1) the primary reasons for a considerable increase in average runoff peaks, using annual maximum runoff discharge and annual maximum precipitation and temperature to evaluate the role of climate variability; (2) the effect of land use change on runoff peaks by coupling dominant land use categories with annual maximum runoff discharge; and (3) the use of return level plots as a reference to explore the watershed's sensitivity to land use change. Our findings indicate that land use change has a greater effect on runoff peak values than climate variability in our region of interest. The agricultural areas of Ferson Creek have been most affected by the rapid transformation of about 20% of their land into developed areas. Although agricultural areas can sometimes intensify runoff peaks, their reduction has led to excessive runoff discharges in Ferson Creek, as they have higher relative infiltration capacity than developed areas. We conclude that each watershed has its own fingerprint in terms of the connection between its land use types and hydrological patterns and that the region is most sensitive to the percentage of forests. These results are essential for improving infrastructure design and risk estimation methods in the region of interest.

Assessment of environmental geological disaster susceptibility under a multimodel comparison to aid in the sustainable development of the regional economy

Environmental geological disasters pose a significant threat to human life, property and environmental safety. It is necessary to conduct targeted governance in key prevention and control areas based on reasonable susceptibility assessment. Using the debris flow disaster in Xiuyan County as an example, this study compares and analyzes prone prediction models such as the frequency ratio (FR), decision tree (DT) and random forest (FR) models and evaluates the cost of prevention and control and the protection of life and property. The research results show that the FR, DT and RF models have good performance. The ROC test, disaster point density statistics and cross-validation results show that the RF model has the best performance. The study area was mainly less and mildly prone areas. The highly prone areas are mainly distributed in the northeast and southwest of the study area. It is the key area of disaster prevention and control. Elevation, rainfall intensity and population density have the largest influence on the susceptibility to debris flows. Based on the RF model, the disaster points in the highly prone areas account for 54% of the disaster points of the whole area, and the project treatment cost of the disaster points is 0.78 million yuan per single gully, which protects 56% of the lives and property in the study area, which is better than the DT and FR models. The RF model not only has good prediction performance in terms of susceptibility. It can realize the targeted management of disasters, achieve the targeted investment of governance costs and the effective protection of life and property and serve the sustainable development of the regional environment and economy with greater value.

Analysis on the susceptibility of environmental geological disasters considering regional sustainable development

Environmental geological disasters seriously threaten human lives and property. A reasonable analysis of the susceptibility to environmental geological disasters is the basis for disaster prevention and mitigation and can promote the sustainable development of regional economy. This study analyzes the susceptibility of environmental geological disasters, such as collapses, landslides, and debris flows in Helong City, China. Through investigation and comprehensive analysis, ten environmental, geological disaster causing factors, including stratum lithology, distance from the fault, elevation, slope, aspect, rainfall, distance from the water system, NDVI, distance from the road, and profile curvature, were extracted. Combined with GIS, a vulnerability analysis database of environmental geological disasters was established, and vulnerability zoning prediction was performed by using two models of information amount and a generalized regression neural network (GRNN). Then, disaster-vulnerability factors such as population density, road density, GDP, and land use type were added. The results show that the predicted results of the two models are similar to the actual survey results.. The environmental geological disasters in the study area are mainly low and not prone to occur, and the northeast and central areas are highly prone to environmental geological disasters, which are the key prevention and control areas in the study area. The coverage rate of high-vulnerability areas with a high degree of economic development is 8.63%, and the prediction results of the GRNN model are mostly distributed in spots and strips, which is more conducive to accurate disaster prevention and mitigation and cost reduction, promotes regional sustainable development, and has guiding significance for disaster prevention and control and urban planning and construction.

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models

The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naïve Bayes (NB). The study was conducted for two watersheds in Canada, namely Lower Nicola River, BC and Loup, QC. Three statistical metrics were used to validate the models: Receiver Operating Characteristic Curve, Figure of Merit, and F1-score. Findings indicated that the RF model had the highest accuracy in providing the flood susceptibility maps (FSMs). Moreover, the provided FSMs indicated that flooding is more likely to occur in the Lower Nicola River watershed than the Loup watershed. Following the RCP4.5 scenario, the area percentages of the flood susceptibility classes in the Loup watershed in 2050 and 2080 have changed by the following percentages from the year 2020 and 2050, respectively: Very Low = -1.68%, Low = -5.82%, Moderate = +6.19%, High = +0.71%, and Very High = +0.6% and Very Low = -1.61%, Low = +2.98%, Moderate = -3.49%, High = +1.29%, and Very High = +0.83%. Likewise, in the Lower Nicola River watershed, the changes between the years 2020 and 2050 and between the years 2050 and 2080 were: Very Low = -0.38%, Low = -0.81%, Moderate = -0.95%, High = +1.72%, and Very High = +0.42% and Very Low = -1.31%, Low = -1.35%, Moderate = -1.81%, High = +2.37%, and Very High = +2.1%, respectively. The impact of climate changes on future flood-prone places revealed that the regions designated as highly and very highly susceptible to flooding, grow in the forecasts for both watersheds. The main contribution of this study lies in the novel insights it provides concerning the flood susceptibility of watersheds in British Columbia and Quebec over time and under various climate change scenarios.

Application of data-mining technique and hydro-chemical data for evaluating vulnerability of groundwater in Indo-Gangetic Plain

Vulnerability of groundwater is critical for the sustainable development of groundwater resources, especially in freshwater-limited coastal Indo-Gangetic plains. Here, we intend to develop an integrated novel approach for delineating groundwater vulnerability using hydro-chemical analysis and data-mining methods, i.e., Decision Tree (DT) and K-Nearest Neighbor (KNN) via k-fold cross-validation (CV) technique. A total of 110 of groundwater samples were obtained during the dry and wet seasons to generate an inventory map. Four K-fold CV approach was used to delineate the vulnerable region from sixteen vulnerability causal factors. The statistical error metrics i.e., receiver operating characteristic-area under the curve (AUC-ROC) and other advanced metrices were adopted to validate model outcomes. The results demonstrated the excellent ability of the proposed models to recognize the vulnerability of groundwater zones in the Indo-Gangetic plain. The DT model revealed higher performance (AUC = 0.97) followed by KNN model (AUC = 0.95). The north-central and north-eastern parts are more vulnerable due to high salinity, Nitrate (NO₃^-), Fluoride (F^-) and Arsenic (As) concentrations. Policy-makers and groundwater managers can utilize the proposed integrated novel approach and the outcome of groundwater vulnerability maps to attain sustainable groundwater development and safeguard human-induced activities at the regional level.

Application of Unmanned Aerial Vehicle DEM in flood modeling and comparison with global DEMs: Case study of Atrak River Basin, Iran

Digital Elevation Models (DEMs) play a significant role in hydraulic modeling and flood risk management. This study initially investigated the effect of Unmanned Aerial Vehicle (UAV) DEM resolutions, ranging from 1 m to 30 m, on flood characteristics, including the inundation area, mean flow depth, and mean flow velocity. Then, the errors of flood characteristics for global DEMs, comprising ALOS (30 m), ASTER (30 m), SRTM (30 m), and TDX (12 m) were quantified using UAV DEM measurements. For these purposes, the HEC-RAS 2D model in steady-state conditions was used to simulate the flood with return periods of 5- to 200 years along 20 km reach of Atrak River located in northeastern Iran. Results indicated when UAV DEM resolution decreased from 1 m to 30 m, inundation area and mean flow depth increased 17.0% (R² = 0.94) and 10.2% (R² = 0.96) respectively, while mean flow velocity decreased 16.8% (R² = -0.94). Validation of the hydraulic modeling using the modified normalized difference water index demonstrated that the HEC-RAS 2D model in conjunction with UAV DEM simulates the flood with ⁓92% accuracy. Comparing the global DEMs with UAV DEM showed that the root mean square error (RMSE) values of the flow depth for ASTER, SRTM, ALOS, and TDX DEMs were 1.77, 1.12, 1.02, and 0.93 m, and the RMSE values of the flow velocity for the same DEMs were 0.81, 0.66, 0.55, and 0.47 m/s, respectively. Furthermore, TDX DEM with a 6.15% error in the inundation area was the nearest to UAV measurements. Overall, TDX DEM revealed a better performance in hydraulic modeling of the fluvial flood characteristics. Hence, it is recommended for environments where high-resolution topography data is scarce. The results of this study could potentially serve as a guideline for selecting global DEMs for hydraulic simulations.

Variation of Net Carbon Emissions from Land Use Change in the Beijing-Tianjin-Hebei Region during 1990–2020

Global increasing carbon emissions have triggered a series of environmental problems and greatly affected the production and living of human beings. This study estimated carbon emissions from land use change in the Beijing-Tianjin-Hebei region during 1990–2020 with the carbon emission model and explored major influencing factors of carbon emissions with the Logarithmic Mean Divisia Index (LMDI) model. The results suggested that the cropland decreased most significantly, while the built-up area increased significantly due to accelerated urbanization. The total carbon emissions in the study area increased remarkably from 112.86 million tons in 1990 to 525.30 million tons in 2020, and the built-up area was the main carbon source, of which the carbon emissions increased by 370.37%. Forest land accounted for 83.58–89.56% of the total carbon absorption but still failed to offset the carbon emission of the built-up area. Carbon emissions were influenced by various factors, and the results of this study suggested that the gross domestic product (GDP) per capita contributed most to the increase of carbon emissions in the study area, resulting in a cumulative increase of carbon emissions by 9.48 million tons, followed by the land use structure, carbon emission intensity per unit of land, and population size. By contrast, the land use intensity per unit of GDP had a restraining effect on carbon emissions, making the cumulative carbon emissions decrease by 103.26 million tons. This study accurately revealed the variation of net carbon emissions from land use change and the effects of influencing factors of carbon emissions from land use change in the Beijing-Tianjin-Hebei region, which can provide a firm scientific basis for improving the regional land use planning and for promoting the low-carbon economic development of the Beijing-Tianjin-Hebei region.

Numerical Analysis of Shallow Foundations with Varying Loading and Soil Conditions

The load–deformation relationship under the footing is essential for foundation design. Shallow foundations are subjected to changes in hydrological conditions such as rainfall and drought, affecting their saturation level and conditions. The actual load–settlement response for design and reconstructions is determined experimentally, numerically, or utilizing both approaches. Ssettlement computation is performed through large-scale physical modeling or extensive laboratory testing. It is expensive, labor intensive, and time consuming. This study is carried out to determine the effect of different saturation degrees and loading conditions on settlement shallow foundations using numerical modeling in Plaxis 2D, Bentley Systems, Exton, Pennsylvania, US. Plastic was used for dry soil calculation, while fully coupled flow deformation was used for partially saturated soil. Pore pressure and deformation changes were computed in fully coupled deformation. The Mohr–Columb model was used in the simulation, and model parameters were calculated from experimental results. The study results show that the degree of saturation is more critical to soil settlement than loading conditions. When a 200 KPa load was applied at the center of the footing, settlement was recored as 28.81 mm, which was less than 42.96 mm in the case of the full-depth shale layer; therefore, settlement was reduced by 30% in the underlying limestone rock layer. Regarding settlement under various degrees of saturation (DOS), settlment is increased by an increased degree of saturation, which increases pore pressure and decreases the shear strength of the soil. Settlement was observed as 0.69 mm at 0% saturation, 1.93 mm at 40% saturation, 2.21 mm at 50% saturation, 2.77 mm at 70% saturation, and 2.84 mm at 90% saturation of soil.

Predicting soil erosion susceptibility associated with climate change scenarios in the Central Highlands of Sri Lanka

Soil erosion hazard is one of the prominent climate hazards that negatively impact countries' economies and livelihood. According to the global climate index, Sri Lanka is ranked among the first ten countries most threatened by climate change during the last three years (2018-2020). However, limited studies were conducted to simulate the impact of the soil erosion vulnerability based on climate scenarios. This study aims to assess and predict soil erosion susceptibility using climate change projected scenarios: Representative Concentration Pathways (RCP) in the Central Highlands of Sri Lanka. The potential of soil erosion susceptibility was predicted to 2040, depending on climate change scenarios, RCP 2.6 and RCP 8.5. Five models: revised universal soil loss (RUSLE), frequency ratio (FR), artificial neural networks (ANN), support vector machine (SVM) and adaptive network-based fuzzy inference system (ANFIS) were selected as widely applied for hazards assessments. Eight geo-environmental factors were selected as inputs to model the soil erosion susceptibility. Results of the five models demonstrate that soil erosion vulnerability (soil erosion rates) will increase 4%-22% compared to the current soil erosion rate (2020). The predictions indicate average soil erosion will increase to 10.50 t/ha/yr and 12.4 t/ha/yr under the RCP 2.6 and RCP 8.5 climate scenario in 2040, respectively. The ANFIS and SVM model predictions showed the highest accuracy (89%) on soil erosion susceptibility for this study area. The soil erosion susceptibility maps provide a good understanding of future soil erosion vulnerability (spatial distribution) and can be utilized to develop climate resilience.

A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications

This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable Artificial Intelligence (XAI) models for data imputations and numerical or categorical hydroclimatic predictions from nonlinearly combined multidimensional predictors. The AI models considered in this paper involve Extreme Gradient Boosting, Light Gradient Boosting, Categorical Boosting, Extremely Randomized Trees, and Random Forest. These AI models can transform into XAI models when they are coupled with the explanatory methods such as the Shapley additive explanations and local interpretable model-agnostic explanations. The review highlights that the IAI models are capable of unveiling the rationale behind the predictions while XAI models are capable of discovering new knowledge and justifying AI-based results, which are critical for enhanced accountability of AI-driven predictions. The review also elaborates the importance of domain knowledge and interventional IAI modeling, potential advantages and disadvantages of hybrid IAI and non-IAI predictive modeling, unequivocal importance of balanced data in categorical decisions, and the choice and performance of IAI versus physics-based modeling. The review concludes with a proposed XAI framework to enhance the interpretability and explainability of AI models for hydroclimatic applications.

Green Synthesis of Nanoparticles by Mushrooms: A Crucial Dimension for Sustainable Soil Management

Soil is the main component in the agroecosystem besides water, microbial communities, and cultivated plants. Several problems face soil, including soil pollution, erosion, salinization, and degradation on a global level. Many approaches have been applied to overcome these issues, such as phyto-, bio-, and nanoremediation through different soil management tools. Mushrooms can play a vital role in the soil through bio-nanoremediation, especially under the biological synthesis of nanoparticles, which could be used in the bioremediation process. This review focuses on the green synthesis of nanoparticles using mushrooms and the potential of bio-nanoremediation for polluted soils. The distinguished roles of mushrooms of soil improvement are considered a crucial dimension for sustainable soil management, which may include controlling soil erosion, improving soil aggregates, increasing soil organic matter content, enhancing the bioavailability of soil nutrients, and resorting to damaged and/or polluted soils. The field of bio-nanoremediation using mushrooms still requires further investigation, particularly regarding the sustainable management of soils.

Numerical Analysis of Piled-Raft Foundations on Multi-Layer Soil Considering Settlement and Swelling

Numerical modelling can simulate the interaction between structural elements and the soil continuum in a piled-raft foundation. The present work utilized a two-dimensional finite element Plaxis 2D software to investigate the settlement, swelling, and structural behavior of foundations during the settlement and swelling of soil on various soil profiles under various load combinations and geometry conditions. The field and laboratory testing have been performed to determine the behavior soil parameters necessary for numerical modelling. The Mohr–Coulomb model is utilized to simulate the behavior of soil, as this model requires very few input parameters, which is important for the practical geotechnical behavior of soil. From this study, it was observed that, as soil is soft and has less stiffness, the un-piled raft was not sufficient to resists and higher loads and exceeds the limits of settlement. Piled raft increases the load carrying capacity of soil, and the lower soil layer has a higher stiffness where the pile rests, decreasing the significant settlement. Further, the effects of (L/d) and (s/d) of the pile and Krs on the settlement are also discussed, detailed numerically under different scenarios. The swelling of expansive soil was also simulated in Plaxis 2D with an application of positive volumetric strain. The above-mentioned parametric study was similarly implemented for the heaving of foundation on expansive soil.

Compaction Characteristics and Permeability of Expansive Shale Stabilized with Locally Produced Waste Materials

Waste is available in an abundant form and goes to landfill without any use, creating threats to the environment. Recent and past studies have used different types of waste to stabilize soil and reduce environmental impacts. However, there is a lack of studies on the combined use of marble dust, rice-husk ash, and saw dust in expansive shale soil. The current study tries to overcome such a gap in the literature, studying the effect of marble dust, rice-husk ash, and saw dust on expansive shale’s compaction characteristics and permeability properties. According to unified soil classification and the AAHTO classification system, the geotechnical properties of natural soil are classified as clay of high plasticity (CH) and A-7-5. Several tests are performed in the laboratory to investigate the compaction characteristics and permeability properties of expansive shale. Moreover, permeability apparatus is used to investigate the permeability properties of soil. In addition, due to the accuracy of the apparatus, the conventional apparatus has been partly modified. The experimental results show that the addition of waste to the soil has significantly improved soil stabilization, increasing permeability and decreasing plasticity indexes. In addition, there is a gradual decrease in the dry density of soil and an increase in the permeability of stabilized soil. Based on the outcomes of the current study, it claims and concludes that these waste materials can be used as soil stabilizers or modifiers, instead of being dumped in landfill, which will provide a green, friendly, and sustainable environment. The current study recommends that future researchers use various wastes in the concrete and soil to improve their compaction and mechanical properties.

Threats of climate change and land use patterns enhance the susceptibility of future floods in India

The main objective of this work is the future prediction of the floods in India due to climate and land change. Human activity and related carbon emissions are the primary cause of land use and climate change, which has a substantial impact on extreme weather conditions, such as floods. This study presents high-resolution flood susceptibility maps of different future periods (up to 2100) using a combination of remote sensing data and GIS modelling. To quantify the future flood susceptibility various flood causative factors, Global circulation model (GCM) rainfall and land use and land cover (LULC) data are envisaged. The present flood susceptibility model has been evaluated through receiver operating characteristic (ROC) curve, where area under curve (AUC) value shows the 91.57% accuracy of this flood susceptibility model and it can be used for future flood susceptibility modelling. Based on the projected LULC, rainfall and flood susceptibility, the results of the study indicating maximum monthly rainfall will increase by approximately 40-50 mm in 2100, while the conversion of natural vegetation to agricultural and built-up land is about 0.071 million sq. km. and the severe flood event area will increase by up to 122% (0.15 million sq. km) from now on.

Assessment of Flood Risk Map under Climate Change RCP8.5 Scenarios in Taiwan

Climate change has exerted a significant global impact in recent years, and extreme weather-related hazards and incidents have become the new normal. For Taiwan in particular, the corresponding increase in disaster risk threatens not only the environment but also the lives, safety, and property of people. This highlights the need to develop a methodology for mapping disaster risk under climate change and delineating those regions that are potentially high-risk areas requiring adaptation to a changing climate in the future. This study provides a framework of flood risk map assessment under the RCP8.5 scenario by using different spatial scales to integrate the projection climate data of high resolution, inundation potential maps, and indicator-based approach at the end of the 21st century in Taiwan. The reference period was 1979–2003, and the future projection period was 2075–2099. High-resolution climate data developed by dynamic downscaling of the MRI-JMA-AGCM model was used to assess extreme rainfall events. The flood risk maps were constructed using two different spatial scales: the township level and the 5 km × 5 km grid. As to hazard-vulnerability(H-V) maps, users can overlay maps of their choice—such as those for land use distribution, district planning, agricultural crop distribution, or industrial distribution. Mapping flood risk under climate change can support better informed decision-making and policy-making processes in planning and preparing to intervene and control flood risks. The elderly population distribution is applied as an exposure indicator in order to guide advance preparation of evacuation plans for high-risk areas. This study found that higher risk areas are distributed mainly in northern and southern parts of Taiwan and the hazard indicators significantly increase in the northern, north-eastern, and southern regions under the RCP8.5 scenario. Moreover, the near-riparian and coastal townships of central and southern Taiwan have higher vulnerability levels. Approximately 14% of townships have a higher risk level of flooding disaster and another 3% of townships will become higher risk. For higher-risk townships, adaptation measures or strategies are suggested to prioritize improving flood preparation and protecting people and property. Such a flood risk map can be a communication tool to effectively inform decision- makers, citizens, and stakeholders about the variability of flood risk under climate change. Such maps enable decision-makers and national spatial planners to compare the relative flood risk of individual townships countrywide in order to determine and prioritize risk adaptation areas for planning spatial development policies.

Scenario-Based Comprehensive Assessment for Community Resilience Adapted to Fire Following an Earthquake, Implementing the Analytic Network Process and Preference Ranking Organization Method for Enriched Evaluation II Techniques

Natural hazards bring significant influences on and socioeconomic loss to cities and communities. Historic events show that fire following earthquake (FFE) is the most influential uncertain disturbance on the urban infrastructure system. Under the FFE scenario, the concept of resilience is widely implemented to make up the shortcomings derived from the traditional disaster management methodology. Resilient cities and communities are required to improve the systemic performance in responding to the FFE. To fulfill these goals, measuring community resilience is an essential work for municipal policy makers. Therefore, this study conducted a comprehensive assessment on community resilience adapted to the FFE scenario. The systematic literature review (SLR) was employed to identify the indicators, and the analytic network process (ANP) technique was implemented to determine their weights. 20 indicators were extracted, and 4 communities that encountered FFE in China were selected for the empirical analysis. Thereafter, the preference ranking organization method for enriched evaluation (PROMETHEE) II technique was selected through using the multicriteria decision analysis (MCDA) methods selection framework to fulfill the comprehensive assessment. The results were discussed and demonstrated with graphical analysis for interactive aid (GAIA) technique. The findings revealed that the G Community won the highest score and had the strongest performance. However, H Community had the lowest score and the weakest performance. The proposed comprehensive methods could benefit the decision-makers and the policy executors achieving the community resilience adapted to the FFE scenario by improving the effective indicators.

A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors

The present work aims to build a unique hybrid model by combining six fuzzy operator feature selection-based techniques with logistic regression (LR) for producing groundwater potential models (GPMs) utilising high resolution DEM-derived parameters in Saudi Arabia’s Bisha area. The current work focuses exclusively on the influence of DEM-derived parameters on GPMs modelling, without considering other variables. AND, OR, GAMMA 0.75, GAMMA 0.8, GAMMA 0.85, and GAMMA 0.9 are six hybrid models based on fuzzy feature selection. The GPMs were validated by using empirical and binormal receiver operating characteristic curves (ROC). An RF-based sensitivity analysis was performed in order to examine the influence of GPM settings. Six hybrid algorithms and one unique hybrid model have predicted 1835–2149 km2 as very high and 3235–4585 km2 as high groundwater potential regions. The AND model (ROCe-AUC: 0.81; ROCb-AUC: 0.804) outperformed the other models based on ROC’s area under curve (AUC). A novel hybrid model was constructed by combining six GPMs (considering as variables) with the LR model. The AUC of ROCe and ROCb revealed that the novel hybrid model outperformed existing fuzzy-based GPMs (ROCe: 0.866; ROCb: 0.892). With DEM-derived parameters, the present work will help to improve the effectiveness of GPMs for developing sustainable groundwater management plans.