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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.

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.

Landslides pose significant threats to ecosystems, lives, and economies, particularly in the geologically fragile Sub-Himalayan region of West Bengal, India. This study enhances landslide susceptibility prediction by developing an ensemble framework integrating Recursive Feature Elimination (RFE) with meta-learning techniques. Seven advanced machine learning models- Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extremely Randomized Trees (ET), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and a Meta Classifier (MC) were applied using Remote Sensing and GIS tools to identify key landslide-conditioning factors and classify susceptibility zones. Model performance was assessed through metrics such as accuracy, precision, recall, F1 score, and AUC of the ROC curve. Among the models, the Meta Classifier (MC) achieved the highest accuracy (0.956) and AUC (0.987), demonstrating superior predictive ability. Gradient Boosting (GB), XGBoost, and RF also performed well, with accuracies of 0.943 and AUC values of 0.987 (GB and XGBoost) and 0.983 (RF). Extremely Randomized Trees (ET) exhibited the highest accuracy (0.946) among individual models and an AUC of 0.985. SVM and LR, while slightly less accurate (0.941 and 0.860, respectively), provided valuable insights, with SVM achieving an AUC of 0.972 and LR achieving 0.935. The models effectively delineated landslide susceptibility into five zones (very low, low, moderate, high, and very high), with high and very high susceptibility zones concentrated in Darjeeling and Kalimpong subdivisions. These zones are influenced by intense rainfall, unstable geological structures, and anthropogenic activities like deforestation and urbanization. Notably, ET, RF, GB, and XGBoost demonstrated efficiency in feature selection, requiring fewer input variables while maintaining high performance. This study establishes a benchmark for landslide susceptibility mapping, providing a scalable and adaptable framework for geospatial hazard prediction. The findings hold significant implications for land-use planning, disaster management, and environmental conservation in vulnerable regions worldwide.

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.

Urban flooding poses a significant threat to lives, infrastructure, and sustainable development, particularly in rapidly expanding Indian cities. This study aims to evaluate urban flood susceptibility in Chhatrapati Sambhajinagar, Maharashtra, India, by integrating geospatial analysis and machine learning techniques. Eleven flood-conditioning parameters—elevation, slope, aspect, rainfall, distance to stream, distance to road, topographic wetness index (TWI), stream power index (SPI), plan curvature, normalized difference vegetation index (NDVI), and land use/land cover (LULC)—were derived from remote sensing and GIS datasets. The models employed include Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART). Multicollinearity analysis (Tolerance > 0.7; and VIF < 1.5) confirmed the independence of predictors. Model calibration was performed using grid search-based hyperparameter tuning with tenfold cross-validation. Among the three algorithms, RF achieved the highest predictive performance (Kappa = 0.887; ROC–AUC = 0.988; PRC–AUC = 0.991), followed by SVM (Kappa = 0.839; ROC–AUC = 0.980; PRC–AUC = 0.984) and CART (Kappa = 0.817; ROC–AUC = 0.954; PRC–AUC = 0.961). The spatial distribution of flood susceptibility indicates that low-lying and river-adjacent areas, particularly along the Kham River, exhibit very high flood risk, whereas western elevated zones show minimal susceptibility. The integration of machine learning models with geospatial datasets effectively delineates flood-prone zones and enhances urban resilience planning. The findings provide valuable insights for policymakers to strengthen urban flood management, drainage planning, and sustainable development strategies in semi-arid Indian cities.

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.

The continual urban growth alters the urban ecological landscape pattern and urban ecosystem functions, posing significant challenges to urban environmental and ecological management. The primary goal of this research is to analyze the dynamic character of urbanization and its impact on urban ecosystem services because of the alteration of land use and land cover in Asansol Municipal Corporation. This study employs various analytical tools, including Shannon's entropy and loss and gain analysis, to assess and quantify the urban growth of the study area over the last two decades (2001, 2011, and 2021). The ecosystem service values (ESVs) have been examined using remote sensing and GIS techniques, which correlate with the global value coefficient to estimate total ecosystem service values and individual ecosystem service functions. The present study finds ESVs have declined over time in the study region. The loss in total and some individual ESV in this study landscape necessitates immediate action to improve urban ecosystem sustainability through proper planning and policy implementation.