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The study prioritises and regionalises sub-watersheds within the Teesta River Basin to evaluate erosion. The study develops a comprehensive framework for assessing erosion susceptibility by integrating morphometric parameters like stream order (U), number of streams (Nu), bifurcation ratio (Rb), stream length (Lu), drainage density (Dd), stream frequency (Fs), basin length (L), basin width (W), length–width ratio (L/W), basin area (Au), basin perimeter (P), elongation ratio (Re), circulatory ratio (Rc), and shape factor (Bs) with Multicriteria decision-making (MCDM) techniques like Preference Selection Index (PSI), Standard Deviation (SD), Criteria importance through intercriteria correlation (CRITIC), Entropy, Method based on the removal effects of criteria (MEREC), and Full Consistency Method (FUCOM). By classifying the sub-watersheds according to their risk levels, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) provides a methodical approach to rank the areas that require intervention in order of importance. Tailored management solutions can be implemented by grouping sub-watersheds into three categories according to the degree of erosion danger. The analysis provides insights for effective resource allocation and identifies high-risk regions (e.g., Subwatershed 19 and Subwatershed 13) that require rapid erosion control measures.

Green tourism is an emerging sustainable approach that needs to be implemented to manage environmental pollution in a particular region. Although the Gangetic West Bengal (GWB) is full of green tourism potential, the green tourism potentiality in this region has not been revealed yet. Therefore, the present research is focused on the delineation of the green tourism potential zone of the GWB using the Analytic Hierarchy Process (AHP) and weighted sum techniques. The whole methodology has been implemented here through a straightforward, concise, and multistep (5-steps) process, which removes the entanglement and intricacy of the traditional AHP technique. At the first step, nine thematic layers are prepared. In the second step, pair-wise comparison matrices are formed following the principle of eigenvector. All thematic layers are reclassified at the third stage, and priorities are assigned to each class. The weighted sum procedure is utilized in the fourth stage to get the green tourism potential map, and the consistency ratio is also checked. Finally, the green tourism potential map is classified into high, moderate, and low categories using natural breaks. About 23.753% area of the GWB is identified as the high green tourism potential zone. The 12.691% area is identified as the low green tourism potential zone, and the rest (63.555% area) are recognized as the moderate green tourism potential zone. Further, the green tourism potential map is validated using the correlation coefficient (R2) determined by the district-wise availability (percentage share) of green tourist spots and the concomitant pixel count (percentage share) of the green tourism potential zone. A high R2 value (R2 ~ 84.5%) is obtained here, and therefore, the green tourism potential map portrayed in this research can be utilized further without hesitation. The methodology used here is generous, logical, unique, and easy to implement in any region.

Channel migration, erosion and accretion are significant geomorphological processes in floodplain regions, impacting both natural and man-made structures. The Sankosh River, a major tributary of the Brahmaputra River, is a good example of a complex and dynamic river system. Erosion and accretion have caused significant problems along the Sankosh River. Local peoples have lost much fertile agricultural land and homesteads in the last few decades, but no related work has been found in this area that highlights this issue addressing erosion and accretion and bankline shifting. The integration of remote sensing and GIS technologies offers a holistic approach to studying river dynamics in this region. Utilizing Landsat TM, ETM+ and OLI satellite data spanning from 1987 to 2021, an investigation was conducted to analyze spatiotemporal variations along the Sankosh River. The modified normalized difference water index, derived from satellite data, is used to assess changes in surface water area. Thirty randomly distributed transects (T1–T30) from three reaches of the Sankosh River served as the basis for the analysis, focusing on river channel morphology, lateral migration, bank erosion and accretion. The findings indicate that from 1987 to 2021, the river eroded approximately 46.50 km 2 of land at an annual rate of 1.37 km 2 /year. In contrast, accretion occurred at a rate of 1.42 km 2 /year, accumulating a total of 48.27 km 2 of land over the same period. The braided nature of the Sankosh River, characterized by a network of numerous branches within its channel, revealed notable shifts in the river’s centerline. In reaches A and C, the centerline shifted eastward, while reach B experienced a westward shift during the study period. Let it be known that the research is mainly focused on the assessment of bank erosion, accretion and lateral migration; it surely gives some valuable information to understand the dynamic behavior of the Sankosh River, enabling us to recognize the imperative for sustainable riverbank management strategies. These strategies aim to strike a balance between preserving river ecosystem integrity and addressing community needs and safety concerns.

Water scarcity and drought management is the burning issue in India and hence needs serious attention of researchers to develop rigorous plan and management. Areas that belong to various plateaus, e.g., Chotanagpur plateau, Deccan plateau, etc., are mostly affected by drought in India. In the past decade, Bankura District of West Bengal, which belongs to northeast part of Chotanagpur plateau, faced severe drought several times. However, the assessment of drought scenario in this area is far from conclusive statement till date. In this paper, we simulate standardized precipitation index (SPI) using double exponential (DE) and Holt–Winter exponential smoothing model (HW) for several time steps (e.g., 3 months, 6 months, 12 months, 24 months and 48 months) in the time period of 1979–2014. The comparative analysis between two models indicates that DE is more accurate one. DE is observed with relatively low root mean squared error (RMSE) and high R 2 value. Furthermore, drought-prone zones are demarcated using combined scores of principal component analysis (PCA) and those combined scores are estimated using actual, HW and DE simulated SPI in several time steps. At the shorter (3 and 6 months) and longer time step (12, 24 and 48 months), the PCA demonstrates almost same results. The western and northwestern blocks of the district are severely affected by drought, and the southern portions are at mild condition. Spatially distributed RMSE in every time steps is also high in northwestern portions of the study region. Our result may be useful to understand the pattern of drought to take necessary action in management of water resources in Bankura District, West Bengal. Moreover, the study uses an unique methodology to simulate and assess meteorological drought, which is applicable in any region of the world.

Landslide susceptibility mapping is crucial for reducing risks in culturally and historically significant areas like the Darjeeling Toy Train route, a UNESCO World Heritage site. In this study, the risk of landslides along this road is evaluated using Geographic Information System (GIS) tools and advanced machine learning models, such as Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Logistic Regression, and Classification and Regression Trees (CART). It uses a set of 512 landslide and non-landslide sites, with a 70:30 split between training and testing. Within the research area, thirteen topographical, hydrological, and geological factors linked to landslides are shown as GIS layers to make maps of landslide susceptibility (LSM). The study area particularly vulnerable to various types of landslides, including debris slides, rock falls, and soil slips. ROC–AUC results show that the SVM model did the best (0.813), followed by GBM (0.807), Logistic Regression (0.797), and CART (0.781). SVM had the highest accuracy rate at 83.2%, followed by GBM at 81.5% and LR at 80.3%. CART had the lowest overall accuracy rate at 78.6%. Furthermore, confusion matrix analysis showed that SVM and Logistic Regression were better at finding actual landslide-prone areas, with 84.6% and 82.1% recall rates, respectively. This made them more accurate in predicting high-risk areas. Susceptibility levels were categorized, revealing high-risk areas like Darjeeling and Rishihat and safer areas like Kurseong and Mohanbari. For lowering the risk of landslides and protecting this historic route, these results are very useful for land management and disaster preparation.

Drought is a burning issue in India and hence needs serious attention of researchers to develop rigorous plan and management. Areas that belong to various plateaus, e.g., Chottanagpur plateau, Deccan plateau, etc., are mostly affected by drought in India. In the past decade, Purulia District of West Bengal, which belongs to northeast part of Chottanagpur plateau, faced severe drought several times. But the assessment of drought in this area was far from a decesive proclamation till date. In this research, an attempt was made to compare the Holt-Winter additive and Holt-Winter multiplicative model in simulation (at 1 month lead time) of meteorological drought (using Standardized Precipitation Index (SPI) of Purulia District, West Bengal, India. The additive model showed better performance than the multiplicative model with minimized Root Mean Squared Error (RMSE) and higher correlation coefficient value (R2). The spatial assessment drought at pre-monsoon, monsoon and post-monsoon phase indicated that severe drought had occurred in post monsoon and premonsoon phase at the eastern portions of the study area.      

Increasing population with increasing demand of groundwater affects the level of groundwater. In the context of considerable change in the use of groundwater pattern, particularly with continuous increase in demand for groundwater due to many reasons, the present paper attempts to delineate groundwater potential zones (GWPZ) using integrated remote sensing, geographic information systems (GIS) and analytic hierarchy process (AHP) methods. To transform and harmonize geographic data and weightage ranking to get reliable information, geographic information systems are combined with analytical hierarchical processes. The current study has been done in the district where many areas are under tea garden and cultivated land. The use of excess of groundwater results in a drop in the water level. The mapping and the identification of groundwater potential zones were done for the Ganga alluvial plain of Alipurduar District of India. The groundwater potential index (GPI) was computed based on several factors (e.g., land use–land cover, soil type, geology, elevation, slope, rainfall, normalized difference vegetation index, drainage density, pre- and post-monsoon groundwater depth, etc.). To generate the groundwater potential zone map of the study area, an overlay weighted sum method was applied to integrate all thematic criteria. Groundwater potential index maps have been classified into five zones. The excellent potential zone comprise 50.5% (1583.68 km 2 ), good 27.4% (859.26 km 2 ), moderate11.3% (354.37 km 2 ), poor 7.1% (222.66 km 2 ) and very poor 3.7% (116.03 km 2 ), respectively. After that, the maps were verified with groundwater-level fluctuation data of 30 observed wells through the ROC (receivers operating characteristic) curve. This paper has important implications for planning the sustainable groundwater plan and also different purposes, such as natural and artificial recharge, watershed delineation and proper water usage, can be effectively implemented in this agriculture-dominated areas in the district.

Asian elephants (Elephas maximus ) are a flagship species in the regional ecosystem and their existence is increasingly threatened by habitat modification. Such circumstance caused Human-Elephant Conflict (HEC) notably in the Alipurduar district of India. This study aims to identify suitable elephant habitats essential for planning effective elephant conservation. The study employed the Analytical Hierarchy Process (AHP) technique and used the resistance surface habitat suitability model in ArcGIS 10.8v software to identify suitable habitats of elephants in the Alipurduar district of India. The findings show 24.01 km² as very high, 110.92 km² as high, and 776.04 km² as moderately suitable habitats for elephants out of a total district area of 3383 km 2 . In related with it, the study reveals that moderate and low suitability zones have the highest human and elephant deaths due to HEC. The suitable habitats of elephants are predominantly identified in the northern and central part of the district, overlapping with the elephant’s corridors, protected areas (PAs) of Buxa Tiger Reserve and Jaldapara National Park. However, the surrounding area of these PAs, with increasing settlements and high anthropogenic pressure, was identified as very low suitable for elephants. The correlation matrix uses for the grid-wise values of each selected parameters with habitat suitability magnitude to show the relation between them. The study further identified that forest cover, forest core, grassland, and water bodies were positively associated with suitable habitats for elephants, whereas variables such as settlements and agricultural lands were negatively associated. We recommend the management focus on the connectivity between these PAs that may be helpful for elephant conservation.

Flood susceptibility mapping is crucial for risk assessment and disaster management, particularly in flood-prone regions such as the Kaljani River Basin in West Bengal, India. This study integrates Principal Component Analysis with Artificial Neural Network and Support Vector Machine to assess flood susceptibility. This study is the first to apply a PCA-ANN and SVM-based hybrid framework to flood susceptibility mapping in the Kaljani River Basin. A multicollinearity test identified high Variance Inflation Factor (VIF) values for elevation and slope, necessitating PCA to reduce redundancy and improve model performance. The PCA-ANN model enhanced accuracy from 56% (ANN) to 81.69%, while the SVM model outperformed both, achieving 91.55% accuracy, with high sensitivity (91.18%) and specificity (91.89%). The flood susceptibility maps revealed that Very High and high-risk zones (412.98 km 2 ) are concentrated in low-lying areas near major rivers, while Moderate zones cover 375.13 km 2 . Very Low and Low susceptibility zones (431.87 km 2 ) correspond to higher elevations with better drainage. Elevation, lineament density, and land use and land cover (LULC) were identified as the most significant flood susceptibility parameters by Geodetector analysis. These results provide important insights for the design of infrastructure in the Kaljani River Basin, targeted flood mitigation strategies, and sustainable land-use management. Highlights PCA-ANN boosted flood prediction accuracy from 56% to 81.69%. SVM achieved 91.55% accuracy with strong sensitivity and specificity. Low-lying areas near rivers cover 412.98 km 2 of high flood risk. Elevation, lineament density, and LULC are key flood risk factors. Nine high-risk villages prioritized for targeted flood mitigation planning.