An Integrated Remote Sensing–GIS–Artificial Intelligence Framework for Groundwater Quality Forecasting in India

Abstract

Background: Groundwater is essential for drinking water, agriculture, industry, and ecological sustainability in India; however, its quality is increasingly affected by geogenic processes, agricultural activities, urbanization, industrialization, and climate variability. Conventional monitoring is spatially limited and resource-intensive. Objective: This study aimed to develop an integrated remote sensing-geographic information system-artificial intelligence  framework for groundwater quality assessment and forecasting in India. Methods: Groundwater-quality observations were integrated with satellite-derived variables, including land-use/land-cover, NDVI, NDWI and land-surface temperature, together with geological, climatic, terrain, hydrogeological, and anthropogenic datasets. Random Forest, XGBoost, Support Vector Regression and Artificial Neural Network models were proposed and evaluated using R², RMSE, MAE, and MAPE. Results: The integrated framework enabled spatial prediction, identification of groundwater quality hotspots, assessment of environmental drivers, and comparison of Artificial Intelligence predictions with conventional interpolation methods. Conclusion: The RS–GIS-AI stands for Remote Sensing, Geographic Information System, and Artificial Intelligence. This combined framework integrates satellite or aerial data capture, spatial mapping and analysis, and smart computer learning algorithms to study the Earth. framework provides a scalable decision-support approach for predictive groundwater monitoring, targeted sampling, contamination risk assessment, and sustainable groundwater management in India.