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.

Environmental planning, hazard monitoring, and coastal management depend critically on accurate shoreline definition. This work utilizes high-resolution unmanned aerial vehicle (UAV) data to develop a deep learning framework based on a Residual U-Net architecture for shoreline semantic segmentation. The proposed model integrates residual learning blocks into the conventional U-Net architecture to enhance gradient flow, improve feature extraction, and preserve fine boundary details in challenging coastal settings. Under a supervised learning framework, the model has been trained and validated using a dataset including UAV-acquired photos and manually annotated shoreline masks. The preprocessed input data has been reinforced by geometric adjustments and contrast normalizing to improve resilience and generalization. The Adam optimizer and binary cross-entropy loss helped the model be trained across 150 epochs. F1-score and Intersection over Union (IoU) measures have been used in quantitative performance evaluation. With a peak validation F1-score of 0.9483 and an IoU of 0.9018, the findings demonstrate that the Residual U-Net achieves great segmentation accuracy, showing robust spatial alignment with ground truth annotations. Visual analysis of the expected masks confirmed the approach's applicability to realworld situations by revealing consistent coastline localization throughout diverse environmental circumstances. This work presents a scalable and accurate method for operational shoreline monitoring, demonstrating the potential of deep residual structures for coastal boundary mapping using UAV platforms. Large-scale geospatial analytics and real-time coastal change detection can both benefit from the framework's extension to multi-temporal and multi-sensor data.