Predicting landslides is becoming a crucial global challenge for sustainable development in mountainous areas. This research compares the landslide susceptibility maps (LSMs) prepared from five GIS-based data-driven bivariate statistical models, namely, (a) Frequency Ratio (FR), (b) Index of Entropy (IOE), (c) Statistical Index (SI), (d) Modified Information Value Model (MIV) and (e) Evidential Belief Function (EBF). These five models were tested in the high landslides-prone humid sub-tropical type Upper Tista basin of the Darjeeling-Sikkim Himalaya by integrating the GIS and remote sensing. The landslide inventory map consisting of 477 landslide locations was prepared, and about 70% of all landslide data was utilized for training the model, and 30% was used to validate it after training. A total of fourteen landslide triggering parameters (elevation, slope, aspect, curvature, roughness, stream power index, TWI, distance to stream, distance to road, NDVI, LULC, rainfall, modified fournier index, and lithology) were taken into consideration for preparing the LSMs. The multicollinearity statistics revealed no collinearity problem among the fourteen causative factors used in this study. Based on the FR, MIV, IOE, SI, and EBF approaches, 12.00%, 21.46%, 28.53%, 31.42%, and 14.17% areas, respectively, identified in the high and very high landslide-prone zones. The research also revealed that the IOE model has the highest training accuracy of 95.80%, followed by SI (92.60%), MIV (92.20%), FR (91.50%), and EBF (89.90%) models. Consistent with the actual distribution of landslides, the very high, high, and medium hazardous zones stretch along the Tista River and major roads. The suggested landslide susceptibility models have enough accuracy for usage in landslide mitigation and long-term land use planning in the study area. Decision-makers and local planners may utilise the study's findings. The techniques for determining landslide susceptibility can also be employed in other Himalayan regions to manage and evaluate landslide hazards.

Groundwater is one of the fundamental sources of fresh water. Currently, the unequal distribution of groundwater poses a significant environmental crisis, leading to scarcity in certain regions and affecting the livelihood of living organisms worldwide. The present study introduces the Sub-Himalayan foothills Dooars, a highly populated and agriculturally based area. To delineate the groundwater potentiality eight different effective parameters have been employed such as slope, geomorphology, soil, LULC, lineament, rainfall, hydrogeology, and drainage density. For the process and development of these eight thematic layers, geographic information systems and satellite images (Landsat 8) have been used to give the normalized weight and rank value in each thematic layer and their sub-criteria. MCDA-AHP-based model is used for overlay analysis to delineate groundwater potentiality (GWPZ). After overlay analysis in ArcGIS 10.2.2 final groundwater potentiality map is further grouped into four potentiality zones, i.e., very high, high, moderate, and low potential zones. A very high potential zone encompasses 22.45 sq. km. in the southern portion of the selected study region. The high potential zone encompasses 1613.32 sq. km in the southeastern part, the moderate zone is covered by 1063.84 sq.km, and the 71.03 sq.km (2.56% area of the region) area is covered by the low potential zone. The available well data from CGWB have been utilized to assess the potential of groundwater in the district, ensuring originality and authentic validation. It is found that there is a correlation between the observed potentiality zone and existing well data. With the growing population in the Sub-Himalayan Dooars region, there has been a corresponding rise in the demand for water. Hence, this research would assist the authorities and policymakers in effectively managing the groundwater resources in the district.