Mining activities in ecologically sensitive and monsoon-dominated regions such as the Goa mining belt impose significant pressures on water resources, air quality, land systems, and tailings infrastructure. This study proposes an integrated Artificial Intelligence (AI)–driven environmental monitoring framework that combines multispectral satellite imagery (Sentinel-1 and Sentinel-2), drone-based surveys, IoT sensor networks, and advanced machine learning and deep learning models to assess and minimize mining-induced environmental impacts. Random Forest, Support Vector Regression, CNN, LSTM, U-Net, DeepLabV3+, and autoencoder models were employed to predict water quality parameters, forecast dust emissions, map land degradation, and monitor tailings dam stability. Results demonstrate strong predictive performance, with water quality models achieving coefficients of determination (R 2 ) of 0.92 for turbidity and 0.88 for total suspended solids, while PM 10 concentration forecasting reached an accuracy of approximately 89% along major haul roads. Deep-learning–based land degradation analysis revealed vegetation loss ranging from 21% to 38% in active mining zones between 2000 and 2023. InSAR–LSTM integration detected millimeter-scale deformation rates of 2–8 mm/month in selected tailings facilities and provided early-warning signals 24–48 h prior to potential instability during peak monsoon periods. All AI outputs were synthesized within a GIS environment to generate spatially explicit environmental risk maps identifying high-risk hotspots in Bicholim, Sanquelim, Sirigao, and Sanguem. The findings confirm that AI-enabled environmental intelligence enables accurate, predictive, and continuous monitoring, offering a robust pathway toward proactive risk mitigation and sustainable, regulatory-compliant mining in Goa and similar environmentally vulnerable regions.
Patil et al. (Thu,) studied this question.