Flash Flood Susceptibility Assessment and Zonation Using an Integrating Analytic Hierarchy Process and Frequency Ratio Model for the Chitral District, Khyber Pakhtunkhwa, Pakistan

Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment

Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due to climate change exacerbating extreme weather events robust flood hazard modeling is crucial to support disaster resilience and adaptation. This study uses multi-sourced geospatial datasets to develop an advanced machine learning framework for flood hazard assessment in the Arambag region of West Bengal, India. The flood inventory was constructed through Sentinel-1 SAR analysis and global flood databases. Fifteen flood conditioning factors related to topography, land cover, soil, rainfall, proximity, and demographics were incorporated. Rigorous training and testing of diverse machine learning models, including RF, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, and MARS algorithms, were undertaken for categorical flood hazard mapping. Model optimization was achieved through statistical feature selection techniques. Accuracy metrics and advanced model interpretability methods like SHAP and Boruta were implemented to evaluate predictive performance. According to the area under the receiver operating characteristic curve (AUC), the prediction accuracy of the models performed was around > 80%. RF achieves an AUC of 0.847 at resampling factor 5, indicating strong discriminative performance. AdaBoost also consistently exhibits good discriminative ability, with AUC values of 0.839 at resampling factor 10. Boruta and SHAP analysis indicated precipitation and elevation as factors most significantly contributing to flood hazard assessment in the study area. Most of the machine learning models pointed out southern portions of the study area as highly susceptible areas. On average, from 17.2 to 18.6% of the study area is highly susceptible to flood hazards. In the feature selection analysis, various nature-inspired algorithms identified the selected input parameters for flood hazard assessment, i.e., elevation, precipitation, distance to rivers, TWI, geomorphology, lithology, TRI, slope, soil type, curvature, NDVI, distance to roads, and gMIS. As per the Boruta and SHAP analyses, it was found that elevation, precipitation, and distance to rivers play the most crucial roles in the decision-making process for flood hazard assessment. The results indicated that the majority of the building footprints (15.27%) are at high and very high risk, followed by those at very low risk (43.80%), low risk (24.30%), and moderate risk (16.63%). Similarly, the cropland area affected by flooding in this region is categorized into five risk classes: very high (16.85%), high (17.28%), moderate (16.07%), low (16.51%), and very low (33.29%). However, this interdisciplinary study contributes significantly towards hydraulic and hydrological modeling for flood hazard management.

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.

Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS

Floods are arguably the most impactful of natural hazards. The increasing magnitude of their effects on the environment, human life, and economic activities calls for improved management of water resources. Flood susceptibility modeling has been used around the world to reduce the damage caused by flooding, although the extrapolation problem still presents a significant challenge. This study develops a machine learning (ML) model utilizing deep neural network (DNN) and optimization algorithms, namely earthworm optimization algorithm (EOA), wildebeest herd optimization (WHO), biogeography-based optimization (BBO), satin bowerbird optimizer (SBO), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO), to solve the extrapolation problem in the construction of flood susceptibility models. Quang Nam Province was chosen as a case study as it is subject to the significant impact of intense flooding, and Nghe An Province was selected as the region for extrapolation of the flood susceptibility model. Root mean square error (RMSE), receiver operating characteristic (ROC), the area under the ROC curve (AUC), and accuracy (ACC) were applied to assess and compare the fit of each of the models. The results indicated that the models in this study are a good fit in establishing flood susceptibility maps, all with AUC > 0.9. The deep neural network (DNN)-BBO model enjoyed the best results (AUC = 0.99), followed by DNN-WHO (AUC = 0.99), DNN-SBO (AUC = 0.98), DNN-EOA (AUC = 0.96), DNN-GOA (AUC = 0.95), and finally, DNN-PSO (AUC = 0.92). In addition, the models successfully solved the extrapolation problem. These new models can modify their behavior to evaluate flood susceptibility in different regions of the world. The models in this study distribute a first point of reference for debate on the solution to the extrapolation problem, which can support urban planners and other decision-makers in other coastal regions in Vietnam and other countries.

Spatial soil loss prediction impacted by long-term land use/land cover change: a case study of Swat District

Soil erosion is a destructive consequence of land degradation caused by deforestation, improper farming practices, overgrazing, and urbanization. This irreversible effect negatively impacts the limited renewable soil resource, causing soil truncation, reduced fertility, and unstable slopes. To address the anticipation of erosion modulus resulting from long-term land use and land cover (LULC) changes, a study was conducted in the Swat District of Khyber Pakhtunkhwa (Kpk), Pakistan. The study aimed to predict and evaluate soil erosion concerning these changes using remote sensing (RS), geographic information systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE) model. We also evaluated the impact of the Billion Tree Tsunami Project (BTTP) on soil erosion in the region. Model inputs, such as rainfall erosivity factor, topography factor, land cover and management factor, and erodibility factor, were used to calculate soil erosion. The results revealed that significant soil loss occurred under 2001, 2011, and 2021 LULC conditions, accounting for 67.26%, 61.78%, and 65.32%, falling within the category of low erosion potential. The vulnerable topographical features of the area indicated higher erosion modulus. The maximum soil loss rates observed in 2001, 2011, and 2021 were 80 t/ha-1/year-1, 120 t/ha-1/year-1, and 96 t/ha-1/year-1, respectively. However, the observed reduction in soil loss in 2021 as compared to 2001 and 2011 suggests a positive influence of the BTTP on soil conservation efforts. This study underscores the potential of afforestation initiatives like the BTTP in mitigating soil erosion and highlights the significance of environmental conservation programs in regions with vulnerable topography.

A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India

In the Sub-Himalayan foothills region of eastern India, floods are considered the most powerful annually occurring natural disaster, which cause severe losses to the socio-economic life of the inhabitants. Therefore, the present study integrated geographic information system (GIS) and three comprehensive and systematic multicriteria decision-making (MCDM) techniques such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacijaik Ompromisno Resenje (VIKOR), and Evaluation Based on Distance from Average Solution (EDAS) in Koch Bihar district for comparative assessment of the flood-susceptible zones. The multi-dimensional 21 indicators were considered, and multicollinearity statistics were employed to erase the issues regarding highly correlated parameters (i.e., MFI and long-term annual rainfall). Results of MCDM models depicted that the riparian areas and riverine "chars" (islands) are the most susceptible sectors, accounting for around 40% of the total area. The microlevel assessment revealed that flooding was most susceptible in the Tufanganj-I, Tufanganj-II, and Mathabhanga-I blocks, while Haldibari, Sitalkuchi, and Sitai blocks were less susceptible. Spearman's rank (r_s) tests among the three MCDM models revealed that TOPSIS-EDAS persisted in a high correlation (r_s = 0.714) in contrast to the relationships between VIKOR-EDAS (r_s = 0.651) and TOPSIS-VIKOR (r_s = 0.639). The model's efficiency was statistically judged by applying the receiver operating characteristic-area under the curve (ROC-AUC), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) techniques to recognize the better-suited models for mapping the flood susceptibility. The performance of all techniques is found good enough (ROC-AUC =  > 0.700 and MAE, MSE and RMSE =  < 0.300). However, TOPSIS and VIKOR have manifested an excellent outcome and are highly recommended for identifying flood susceptibility in such active flood-prone areas. Thus, this kind of study addresses the role of GIS in the construction of the flood susceptibility of the region and the performance of the respective models in a very lucid manner.

Flood vulnerability mapping and urban sprawl suitability using FR, LR, and SVM models

Floods are among the most destructive disasters because they cause immense damage to human life, property (land and buildings), and resources. They also slow down a country's economy. Due to the dynamic and complex nature of floods, it is difficult to predict the areas that are prone to flooding. In this study, an attempt was made to create a suitability map for future urban development based on flood vulnerability maps for the catchment area of Taif, Saudi Arabia. Three models were used for this purpose, including bivariate (FR), multivariate (LR), and machine learning (SVM) were used. Thirteen parameters were used as flood-contributing parameters. The inventory map was constructed using field surveys, historical data, analysis of RADAR (Sentinel-1A), and Google Earth imagery collected between 2013 and 2020. In general, 70% flood locations were randomly selected from the flood inventory map to generate the flood susceptibility model, and the remaining 30% of the flood locations were used for model validation. The flood susceptibility map was classified into five zones: very low, low, moderate, high, and very high. The AUC value used to predict the performance of the models showed that the accuracy reached 89.5, 92.0, and 96.2% for the models FR, LR, and SVM, respectively. Accordingly, the flood susceptibility map produced by the SVM model is accurate and was used to produce a flood vulnerability map with the help of urban and road density maps. Then slope and elevation maps were integrated with the flood vulnerability model to produce the final suitability map, which was classified into three zones: isolated zone, low suitability, and high suitability areas. The results showed that the highly suitable areas are located in the east and northeast of the Taif Basin, where the flood risk is low and very low. The results of this work will improve the land use planning of engineers and authorities and take possible measures to reduce the flood hazards in the area.

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.

Fusion of Remote Sensing Data Using GIS-Based AHP-Weighted Overlay Techniques for Groundwater Sustainability in Arid Regions

Remote sensing and GIS approaches have provided valuable information on modeling water resources, particularly in arid regions. The Sahara of North Africa, which is one of the driest regions on Earth, experienced several pluvial conditions in the past that could have stored significant amounts of groundwater. Thus, harvesting the stored water by revealing the groundwater prospective zones (GWPZs) is highly important to water security and the management of water resources which are necessary for sustainable development in such regions. The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR), Tropical Rainfall Measuring Mission (TRMM), and Landsat-8 OLI data have all successfully revealed the geologic, geomorphic, climatic, and hydrologic features of Wadi El-Tarfa east of Egypt’s Nile River. The fusion of eleven predictive GIS maps including lithology, radar intensity, lineament density, altitude, slope, depressions, curvature, topographic wetness index (TWI), drainage density, runoff, and rainfall data, after being ranked and normalized through the GIS-based analytic hierarchy process (AHP) and weighted overlay methods, allowed the GWPZs to be demarcated. The resulting GWPZs map was divided into five classes: very high, high, moderate, low, and very low potentiality, which cover about 10.32, 24.98, 30.47, 24.02, and 10.20% of the entire basin area, respectively. Landsat-8 and its derived NDVI that was acquired on 15 March 2014, after the storm of 8–9 March 2014, along with existing well locations validated the GWPZs map. The overall results showed that an integrated approach of multi-criteria through a GIS-based AHP has the capability of modeling groundwater resources in arid regions. Additionally, probing areas of GWPZs is helpful to planners and decision-makers dealing with the development of arid regions.

Monitoring land use land cover changes and its impacts on land surface temperature over Mardan and Charsadda Districts, Khyber Pakhtunkhwa (KP), Pakistan

Land use/land cover (LULC) changes due to urban growth on the regional scale affect land surface temperature (LST). The present study aims to assess the LULC changes and their impact on LST over Mardan and Charsadda districts of Khyber Pakhtunkhwa (KP), Pakistan, in the period from 1990 to 2019. Landsat satellite (TM& ETM +) datasets in the period from 1990 to 2010 and Sentinel-2 images from 2016 to 2019 were used in this study. All the datasets were pre-processed and the LULC types were classified by maximum likelihood classification algorithm. The vegetation degradation was computed from normalized difference vegetation index (NDVI), and the LST was derived based on the LULC changes. The results showed that the overall accuracy of LULC classification was 87.84%. Dramatic LULC changes were observed during the last three decades, where the vegetation degradation area was decreased from 1307.8 (59.27%) to 1147.6 km² (52.1%) and the barren land area increased from 816.6 (37.07%) to 961.4 km² (42.64%). Similarly, the built-up area has also increased from 57.2 (2.5%) to 104.3 km² (4.73%) in the years 1990 and 2019, respectively. These variations in LULC types have significantly influenced the LST from 1990 to 2019; specifically, the LST of built-up area, barren land, and vegetation cover increased from 20.1 to 32.1 °C, 21.5 to 35.5 °C, and 17.1 to 28.2 °C, respectively. The regression line plotted defines that the LST has a negative correlation with NDVI and a positive correlation with normalized difference of built-up index (NDBI). In particular, the vegetation and land covers dramatically transformed to barren land and/or to urban development over the study area in the period from 1990 to2019, which has severely affected the LST and the natural resources of the study area. Therefore, our study will be very helpful for managing the rapid environmental changes and urban planning.

Spatio-Temporal Extraction of Surface Waterbody and Its Response of Extreme Climate along the Upper Huaihe River

The upper Huaihe River is the water-producing area of the Huaihe River Basin and the major grain and oil-producing area in China. The changing global climate over the recent years has increased the frequency of extreme weather in the upper reaches of the Huaihe River. Research on the responses of surface water bodies to extreme climates has become increasingly important. Based on all utilizable Landsat 4–8 T1–SR data and frequency mapping, the spatio-temporal extraction of surface water and its response to extreme climate were studied. We generated high-precision frequency maps of surface water, and a comparison of cartographic accuracy evaluation indices and spatial consistency was also carried out. The high-precision interpretation of small waterbodies constructs a surface water distribution with better continuity and integrity. Furthermore, we investigated the effect of El Niño/La Niña events on precipitation, temperature, and surface water along the upper Huaihe River, using the Mann–Kendall mutation tests. The results show: in 1987–2018, periods of abrupt changes in precipitation coincide with EI Niño/La Niña events, indicating that the precipitation was sensitive to EI Niño/La Niña events, which also strongly correlated with surface water area during wet and dry years. The effect of extreme events on seasonal water was smaller than permanent water. Surface water area showed an insignificant declining trend after 1999 and a significant drop in 2012. The phenomenon of topographic enhancement of precipitation controlled the spatial distribution of permanent water, with human activities having a substantial effect on the landscape pattern of seasonal water. Finally, discussions and applications related to the Markov Chain probability calculation theory in the paper contributed to enriching the theories on frequency mapping. The relevant results provide a theoretical basis and case support for the formulation of long-term water resources utilization and allocation policies.

Classification of Aquifer Vulnerability by Using the DRASTIC Index and Geo-Electrical Techniques

Vulnerability analysis in areas vulnerable to anthropogenic pollution has become a key element of sensible resource management and land use planning. This study is intended to estimate aquifer vulnerability using the DRASTIC model and using the vertical electrical sounding (VES) and electrical conductivity (EC) outcomes. The model allows for the identification of hydrogeological environments within the scope of the research, based on a composite definition of each environment’s main geological, geoelectrical, and hydrogeological factors. The results from the DRASTIC model were divided into four equal intervals, high, medium, low, and very low drastic index values. The SW area and NE area depict drastic index values from medium to very high, making it the most vulnerable zone in the study area, while the NW and SW areas show low to very low drastic index values. In addition, the results from the VES and EC the freshwater aquifer in the NE area and brackish water in the SE area, while the rest of the area falls into the category of brackish water. Overall, it can be concluded that areas having freshwater assemblages are on the verge of becoming contaminated in the future while the rest of the NW and SW areas constitute less vulnerable zones. The validation conducted for DRASTIC and EC shows a nearly positive correlation. Wastewater treatment policies must be developed throughout the studied region to prevent contamination of the remaining groundwater.