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Abstract Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies. In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond.

Climate change has substantially increased both the occurrence and intensity of flood events, particularly in the Indian subcontinent, exacerbating threats to human populations and economic infrastructure. The present research employed novel ML models—LR, SVM, RF, XGBoost, DNN, and Stacking Ensemble—developed in the Python environment and leveraged 18 flood-influencing factors to delineate flood-prone areas with precision. A comprehensive flood inventory, obtained from Sentinel-1 Synthetic Aperture Radar (SAR) data using the Google Earth Engine (GEE) platform, provided empirical data for entire model training and validation. Model performance was assessed using precision, recall, F1-score, accuracy, and ROC-AUC metrics. The results highlighted Stacking Ensemble’s superior predictive ability (0.965), followed closely by, XGBoost (0.934), DNN (0.929), RF (0.925), LR (0.921), and SVM (0.920) respectively, establishing the feasibility of ML applications in disaster management. The maps depicting susceptibility to flooding generated by the current research provide actionable insights for decision-makers, city planners, and authorities responsible for disaster management, guiding infrastructural and community resilience enhancements against flood risks.

AbstractGroundwater is an essential natural resource that sustains numerous ecological systems and human lifestyles. The Jhargram district is facing persistent groundwater development issues, requiring comprehensive monitoring and planning, as most farmers heavily rely on groundwater for crop production. The groundwater potential zone of the Jhargram district was classified into five classes, viz., very high (5.82%), high (50.81%), moderate (30.33%), low (13.01%), and very low (0.03%), zones, respectively. The results were assessed for validity using ROC curves, which demonstrated an accuracy rate of 80.4%. The calculation of the AUC was performed in order to assess the overall accuracy of the predicted GWPZ. The GWPZ map is crucial for implementing artificial recharge structures like percolation ponds, bunds and trenching in semi-arid regions. It aids in developing sustainable groundwater management policies, mitigating drought, climate change, and water scarcity, and aiding farmers, regional planners, policy-makers, climate change experts, and local governments.

AbstractFloods are a recurrent natural calamity that presents substantial hazards to human lives and infrastructure. The study indicates that a significant proportion of the study area, specifically 27.05%, is classified as a moderate flood risk zone (FRZ), while 20.78% is designated as high or very high FRZ. The region’s low and very low FRZ are classified at 52.17%. The GIS-based AHP model demonstrated exceptional predictive precision, achieving a score of 0.749 (74.90%) as determined by the AUC-ROC, a widely used statistical evaluation tool. The current study has identified areas with high FRZ in the affected CD blocks, which are situated in low-lying flood plains, regions with gentle slopes, high drainage density, high TWI, low NDVI, high MNDWI, areas with high population density, intensive agricultural land. The findings of this research offer significant perspectives for decision-makers, city planners, and emergency management agencies in devising efficient measures to mitigate flood risks.

Abstract Shortage of potable water is a global problem, and this problem can be met by searching new areas where groundwater is available. GIS is an effective and necessary tool to identify groundwater potential zones in an area. In the present study, groundwater potential zones (GWPZs) were identified in the Kangsabati River basin of east India having an area of about 6488 km2 using multi-criteria decision analysis (MCDA) and analytical hierarchy process (AHP). The criteria like geology, geomorphology, elevation, slope, drainage, lineament, curvature, topographic wetness, land use/land cover, and soil were extracted from satellite data and the weights for each parameter and its sub-parameters were assigned through analytical hierarchy process based on their respective relevance as influencing factors for groundwater recharge. Very low, low, moderate, high, and very high groundwater potentiality represent 28.93%, 30.56%, 19.75%, 14.62%, and 6.11% area, respectively. The low-lying flat plains of the southeastern section, as well as the centrally located dam, are ideal for groundwater recharge, while the upland plain of the northwestern part, with its hard rock terrain, is less so. This outcome has been verified using pre-monsoon and post-monsoon groundwater depth data, indicating that the strategy is most appropriate for this region. Thus, the groundwater potential zone maps remain very useful for conducting extensive ground-based hydrogeological studies that facilitate the identification of suitable bore well/dug well sites.

Abstract The global economic activities were completely stopped during COVID-19 lockdown and continuous lockdown partially brought some positive effects for the health of the total environment. The multiple industries, cities, towns and rural people are completely depending on large tropical river Damodar (India) but in the last few decades the quality of the river water is being significantly deteriorated. The present study attempts to investigate the river water quality (RWQ) particularly for pre- lockdown, lockdown and unlock period. We considered 20 variables per sample of RWQ data and it was analyzed using novel Modified Water Quality Index (MWQI), Trophic State Index (TSI), Heavy Metal Index (HMI) and Potential Ecological Risk Index (RI). Principal component analysis (PCA) and Pearson’s correlation (r) analysis are applied to determine the influencing variables and relationship among the river pollutants. The results show that during lockdown 54.54% samples were brought significantly positive changes applying MWQI. During lockdown, HMI ranged from 33.96 to 117.33 with 27.27% good water quality which shows the low ecological risk of aquatic ecosystem due to low mixing of toxic metals in the river water. Lockdown effects brought river water to oligotrophic/meso-eutrophic condition from eutrophic/hyper-eutrophic stage. Rejuvenation of river health during lockdown offers ample scope to policymakers, administrators and environmentalists for restoration of river health from huge anthropogenic stress.