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Soil is the earth’s fragile skin that anchors all life on earth. Half of the topsoil on the planet has been lost in the last 150 years. Land degradation due to soil loss is one of the major environmental concerns which can be influences by the natural as well as anthropogenic activities. These impacts include compaction, loss of soil structure, nutrient degradation, and soil salinity. The effects of soil erosion go beyond the loss of fertile land. It has led to increased pollution and sedimentation in streams and rivers. And degraded lands are also often less able to hold onto water which can worsen flooding. Revised Universal soil Loss Equation (RUSLE) model and integration with Geographical Information System (GIS) have been taken into consideration for estimating the average annual soil loss in Arkosa watershed. The Overlay Analysis technique have been adopted in RUSLE model for estimating the influences of different factors namely rainfall and runoff erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover and management factor (C) and support practice factor (P) etc. The average annual soil loss of Arkosa watershed ranged between 0 to 10 tons/ha/year. Here the combined index method has been adopted to show the impact spatially of combine index of these five factors, i.e., R, K, LS, C and P. Apart from this there are total 29 points have been selected randomly for securing that the present soil loss model sounded with ground reality or not. The actual soil loss and predicted soil loss show the positive relationship with them in an r2 value of 0.882. Besides this the present study provides a reliable prediction for future on potential soil erosion risk zones which ranged between 0 and 16 tons/ha/year. To overcome from extreme or severe soil loss situation suitable soil conservation practices or support practices have to be taken care off for minimizing the erosion of the fertile soil or the top soil for making the region less vulnerable from soil erosion in present rate. Sustainable land use can help to reduce the impact of agriculture and livestock, preventing the soil degradation and erosion and the loss of valuable land to deforestation.

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

Remote sensing and geographical information system (RS-GIS) have become a leading tool for modeling and mapping of groundwater resources. An attempt has been made to delineate the groundwater potential zones of Puruliya district using the integrated RS-GIS and AHP techniques. All the themes and their features have been assigned weights according to their relative importance and their normalized weights were calculated after the hierarchical ranking by pair-wise comparison matrix of analytical hierarchy process (AHP). Groundwater potential map has been prepared through weighted overlay model in GIS environment after integrating all the thematic layers. The entire district has been classified into three different groundwater potential zones-high, moderate, and low. Greater portion of the study area (60.92%) fall within the moderate potentiality zone, about 22.55% and 16.53% of the total area fall under the high and low potential zone, respectively. Potential zones have been validated with the groundwater yield data, 10 out of 14 validation points (71.43%), matches with the expected yield classes. It shows that the applied method produces significantly reliable results for the present study which can help the decision makers to formulate an effective plan for the study area.

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.

The exponential growth in the number of flash flood events is a global threat, and detecting a flood-prone area has also become a top priority. The flash flood-susceptibility mapping can help to mitigate the worst effects of this type of risk phenomenon. However, there is an urgent need to construct precise models for predicting flash flood-susceptibility mapping, which can be useful in developing more effective flood management strategies. In this present research, support vector regression (SVR) was coupled with two meta-heuristic algorithms such as particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA), to construct new GIS-based ensemble models (SVR–PSO and SVR–GOA) for flash flood-susceptibility mapping (FFSM) in the Gandheswari River basin, West Bengal, India. In this regard, 16 topographical and environmental flood causative factors have been identified to run the models using the multicollinearity (MC) test. The entire dataset was divided into 70:30 for training and validating purposes. Statistical measures including specificity, sensitivity, PPV, NPV, AUC–ROC, kappa and Taylor diagram have been employed to validate adopted models. The SVR-based factor importance analysis was employed to choose and prioritize significant factors for the spatial analysis. Among the three modeling approaches used here, the ensemble method of SVR–GOA is the most optimal (specificity 0.97 and 0.87, sensitivity 0.99 and 0.91, PPV 0.97 and 0.86, NPV 0.99 and 0.91, AUC 0.951 and 0.938 in training and validation, respectively), followed by the SVR–PSO (specificity 0.84 and 0.84, sensitivity 0.87 and 0.86, PPV 0.85 and 0.82, NPV 0.87 and 0.87, AUC 0.951 and 0.938 in training and validation, respectively) and SVR (specificity 0.80 and 0.77, sensitivity 0.93 and 0.89, PPV 0.82 and 0.77, NPV 0.91 and 0.89, AUC 0.951 and 0.938 in training and validation, respectively) model. The result shown that 40.10 km2 (10.99%) and 25.94 km2 (7.11%) areas are under very high and high flood-prone regions, respectively. This produced reliable results that can help policymakers at the local and national levels to implement a concrete strategy with an early warning system to reduce the occurrence of floods in a region.

Landslide susceptibility predictive capabilities are believed to be varied with numerous techniques such as stand-alone statistical, stand-alone machine learning (ML), and ensemble of statistical and ML. However, the landslide susceptibility (LS) model is constantly being modified with recent progress in statistics and in ML. We used logistic regression (LR), random forest (RF), boosted regression tree (BRT), BRT-LR, and BRT-RF model for model calibration and validation. Apart from that, we used RF to measure the relative importance of landslide causative factors (LCFs). Tests were conducted to the damaged landslide patches using a number of 16 LCFs (geomorphological, hydrological, geological, and environmental). We noticed that the predicted rates are exceptional for the BRT-RF model (AUC: 0.919), whereas models of LR (0.822), RF (0.876), BRT (0.857), and BRT-LR (0.902) produced higher variations in the data set accuracy. We therefore propose that the BRT-RF model be an effective method of increasing predictive precision level of LS. This research finding can be used in other fields for planning and management by stakeholders in order to minimize the impact of landslide.

Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken to minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone to flash flooding is a crucial step in flash flood hazard management. In the present study, the Kalvan watershed in Markazi Province, Iran, was chosen to evaluate the flash flood susceptibility modeling. Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and geo-environmental variables were used as inputs of the flash flood susceptibility models. The results showed that ERT was the most optimal model with an area under curve (AUC) value of 0.82. The rest of the models’ AUC values, i.e., RRF, PRF, RF, and BRT, were 0.80, 0.79, 0.78, and 0.75, respectively. In the ERT model, the areal coverage for very high to moderate flash flood susceptible area was 582.56 km2 (28.33%), and the rest of the portion was associated with very low to low susceptibility zones. It is concluded that topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters. The results of this study will play a vital role in the planning and implementation of flood mitigation strategies in the region.

The landslide is a downward movement of soil and rock, and one of the most destructive geo-hazards that causes losses in lives, environment, and economy all over the world. Landslide susceptibility mapping is a scientific method to evaluate the landslide probability zones and causative factors. The main objective of the present study was to introduce ensemble landslide susceptibility models which are developed on the basis of two statistical models (evidential belief function and geographically weighted regression) and one machine learning model (random forest) for spatial prediction of landslide of the Upper Rangit River Basin, Sikkim, India. Totally, 102 landslide locations have been identified and randomly classified into 70% and 30% as training and validating database, respectively. Total 16 landslide causative factors are considered and grouped into four categories: geomorphological, hydrological, geological, and environmental factors. The evidential belief function (EBF), geographically weighted regression (GWR), and random forest (RF) method and their ensemble methods, RF-EBF, and RF-GWR models have been applied with the help of training landslide and non-landslide dataset and spatial database of landslide causative factors. Five landslide susceptibility maps have been generated by the said model, and the maps have been validated by validating dataset with the help of sensitivity, specificity, accuracy, Kappa index, and area under curve (AUC) of receiver operating characteristic (ROC) tools. The ensemble methods have the best degree-of-fit and prediction performance than single methods, i.e., RF-EBF and RF-GWR model have 91.8% and 89.9% prediction capabilities. The result of the relative importance of factor showed that land use land cover (LULC), distance to river, soil, drainage density, and road density factors have played the key role in the occurrence of the landslide. The result of the study can be used by local planning, dicession makers, and the methods of landslide susceptibility can be applied in other areas.

At present, flood is the most significant environmental problem in the entire world. In this work, flood susceptibility (FS) analysis has been done in the Dwarkeswar River basin of Bengal basin, India. Fourteen flood causative factors extracted from different datasets like DEM, satellite images, geology, soil and rainfall data have been considered to predict FS. Three heuristic models and one statistical model fuzzy Logic (FL), frequency ratio (FR), multi-criteria decision analysis (MCDA) and logistic regression (LR) have been used. The validating datasets are used to validate these models. The result shows that 68.71%, 68.7%, 60.56% and 48.51% area of the basin is under the moderate to very high FS by the MCDA, FR, FL and LR, respectively. The ROC curve with AUC analysis has shown that the accuracy level of the LR model (AUC = 0.916) is very much successful to predict the flood. The rest of the models like FL, MCDA and FR (AUC = 0.893, 0.857 and 0.835, respectively) have lesser accuracy than the LR model. The elevation was the most dominating factor with coefficient value of 19.078 in preparation of the FS according to the LR model. The outcome of this study can be implemented by local and state authority to minimize the flood hazard.

This study evaluates the impacts of projected sea level rise (SLR) on coastal flooding across major Indian cities: Mumbai, Kolkata, Chennai, Visakhapatnam, Surat, Kochi, Thiruvananthapuram, and Mangaluru. Machine learning models, including Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GB), has been employed to assess flood risks under four Shared Socioeconomic Pathways (SSP 126, 245, 370, and 585) emission scenarios. The research utilized these models because they demonstrate high performance in handling difficult data relationships and both temporal patterns and sophisticated environmental data. SLR projections provided by computers generate forecasts that combine with digital elevation models (DEMs) to determine coastal flooding risks and locate flood-prone areas. Results reveal that Mumbai and Kolkata face the highest flood risks, particularly under high emission scenarios, while Kochi and Mangaluru exhibit moderate exposure. Model performance is validated using residual analysis and Receiver Operating Characteristic (ROC) curves, confirming reliable predictive accuracy. These findings provide essential information for urban planners and policymakers to prioritize climate adaptation strategies in vulnerable coastal cities.