The Pandameru River Basin, South India, is affected by high levels of contamination from human activities and the over-exploitation of groundwater for agriculture, both of which pose significant threats to water quality and its availability for drinking and irrigation. To explore sustainable groundwater management, this study presents a machine learning-driven approach to basin-scale groundwater potential zone (GWPZ) mapping by integrating remote sensing (RS), a geographic information system (GIS), and the random forest (RF) algorithm. The research leverages ten thematic layers—including lithology, geomorphology, soil type, lineament density, slope, drainage density, land use/land cover (LULC), NDVI, SAVI, and rainfall—to assess groundwater availability. The RF model, trained with well-distributed groundwater data, provides an optimized classification of GWPZs into five categories: very good (5.84%), good (15.21%), moderate (27.25%), poor (27.22%), and very poor (24.47%). The results indicate that excellent groundwater zones are predominantly located along highly permeable alluvial deposits, whereas low-potential zones coincide with impermeable geological formations and steep terrains. Field validation using piezometric readings and well data confirmed significant variations in water table depths, ranging from 5 m to over 150 m. The groundwater potential map achieved an accuracy of 86%, underscoring the effectiveness of the RF model in predicting groundwater availability. This high-precision mapping technique enhances decision-making for sustainable groundwater management, supporting long-term water conservation, equitable resource allocation, and climate-resilient water strategies. By providing reliable insights into groundwater distribution, this study contributes to the sustainable utilization of groundwater resources in semiarid regions, aiding policymakers and planners in mitigating water scarcity challenges and ensuring water security for future generations.

Landslides are among the most destructive natural hazards in the Western Ghats region of Kerala, driven by complex interactions between geological, hydrological, and anthropogenic factors. This study aims to generate a high-resolution Landslide Susceptibility Level Map (LSLM) using a machine learning (ML)-based Random Forest (RF) model integrated with Geographic Information Systems (GIS). A total of 231 historical landslide locations obtained from the Bhukosh portal were used as reference data. Eight predictive factors—Stream Order, Drainage Density, Slope, Aspect, Geology, Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Moisture Stress Index (MSI)—were derived from remote sensing and ancillary datasets, preprocessed, and reclassified for model input. The RF model was trained and validated using a 50:50 split of landslide and non-landslide points, with variable importance values derived to weight each predictive factor of the raster layer in ArcGIS. The resulting Landslide Susceptibility Index (LSI) was reclassified into five susceptibility zones: Very Low, Low, Moderate, High, and Very High. Results indicate that approximately 17.82% of the study area falls under high to very high susceptibility, predominantly in the steep, weathered, and high rainfall zones of the Western Ghats. Validation using Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) analysis yielded an accuracy of 0.890, demonstrating excellent model performance. The output LSM provides valuable spatial insights for planners, disaster managers, and policymakers, enabling targeted mitigation strategies and sustainable land-use planning in landslide-prone regions.

Assessing the ecological environmental quality (EEQ) is crucial for protecting the environment. Dhaka’s rapid, unplanned urbanization, driven by economic and social growth, poses significant eco-environmental challenges. Spatiotemporal ecological and environmental quality changes were assessed using remote sensing based ecological index (RSEI) maps derived from Landsat images (1993, 2003, 2013, and 2023). RSEI was based on four indicators—greenness (NDVI), heat index (LST), dryness (NDBSI), and wetness (LSM). Landsat 5 TM and 8 OLI/TIRS images were processed on Google Earth Engine (GEE), with principal component analysis (PCA) applied to determine RSEI. The findings showed a decline in the overall RSEI (1993–2023), with low- and very low-quality areas increasing by about 39% and high- and very high-quality areas decreasing by 24% of the total area. NDBSI and LST were negatively correlated with RSEI, except in 1993, while NDVI and LSM were generally positive but negative in 1993. The global Moran’s I (0.88–0.93) indicated strong spatial correlation in the distribution of EEQ across Dhaka. LISA cluster maps showed high-high clusters in the northeast and east, while low-low clusters were concentrated in the northwest. This research examines the degradation of ecological conditions over time in Dhaka and provides valuable insights for policymakers to address environmental issues and improve future ecological management.

Sub-watershed prioritization using morphometric analysis and multi-criteria decision-making (MCDM) techniques is a systematic approach to identifying and ranking sub-watersheds based on their susceptibility to soil erosion. This helps in implementing targeted soil conservation measures. In this study, sub-watersheds in the Narangi basin are prioritized by employing morphometric analysis integrated with advanced MCDM techniques, including additive ratio assessment (ARAS), complicated proportional assessment (COPRAS), multi-objective optimization by ratio analysis (MOORA), and the technique for order preference by similarity to ideal solution (TOPSIS). Weights for various MCDM methods are determined using the criteria importance through an inter-criteria correlation approach (CRITIC: criteria importance through inter-criteria correlation method), while geospatial techniques ensure precise spatial analysis. The results provide a unified ranking of sub-watersheds, revealing that sub-watershed 3 (SW3) and SW9 are in the high-priority soil erosion category; SW1, SW2, SW5, and SW8 are medium-priority; and SW4, SW6, SW7, and SW10 are low-priority. This comprehensive and sustainability-oriented approach equips decision-makers with robust tools to identify and manage sub-watersheds at risk of soil erosion, ensuring the long-term sustainability of land and water resources. This study aligns with sustainable development goal 15 (life on land) and promotes sustainable land use practices to combat soil degradation.

Due to the increase in demand for water, the rapid growth of urbanization and industrialization is the main threat to the source and quality of groundwater. The present study aimed to assess the suitability of groundwater for agricultural purposes in coastal regions using integrated approaches such as the saltwater mixing index (SWMI), the mineral saturation index (MSI), the agriculture suitability index (ASI), and unsupervised machine learning (USML) techniques. The result of the SWMI revealed that 20 and 17 sample locations were highly affected by saltwater intrusion in the study region’s northern and southeastern parts during the pre- and post-monsoon seasons. The detailed analysis of electrical conductivity in groundwater revealed that 19.64% and 14.29% of the samples were unfit for irrigation purposes, especially five sample locations, during both seasons. Regarding the overall suitability of groundwater for irrigation uses, the ASI values divulged that 8.9% of the samples were unsuitable for irrigation purposes. The spatial analysis of the ASI value indicated that 43.19 and 85.33 sq. km of area were unsuitable for irrigation practices. Additionally, the USML techniques identified the most influenced parameters such as Ca²⁺, Mg²⁺, Cl⁻, and SO₄²⁻ during both seasons. The present study results help maintain proper, sustainable water management in the study region.

Ecotourism is becoming more and more significant because it aids in environmental protection and maintaining the sustainable growth of a region. Mizoram is known for its potentially varied landscapes, which draw visitors from many nations and territories. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach was used in this study to evaluate land suitability for ecotourism initiatives in Mizoram spatially. The study also focused on role weighting (subjective, objective, and intergraded) on the decision-making process. In this regard, the weightage of twelve ecotourism influencing factors was determined to integrate with the TOPSIS model and the Geographical Information System (GIS) environment. As a result, five hierarchical ecotourism zones, including very high to very low, have been classified using Jenks’s natural breaking classification. The model’s accuracy based on the area under the curve (AUC) and receiver operating characteristic (ROC) curve revealed that all models successfully predict potential ecotourism in the marginal hilly region. As a result, the intergrade weighting combined TOPSIS model showed that 25.18% of the study region has very highly suitable for ecotourism. The results of this study may be used as a foundation for assessing the feasibility of resources suitable for ecotourism development by government officials and planners.