An integrated artificial intelligence (AI)-driven framework has been developed to monitor and reduce the environmental impacts of mining operations. To achieve this, researchers combined satellite imagery, drone surveys, Internet of Things (IoT) sensors, machine learning, deep learning, and Geographic Information Systems (GIS) into a single environmental monitoring framework.
Study: Leveraging artificial intelligence for minimizing environmental footprints in the mining industry. Image Credit: Parilov/Shutterstock.com
The AI models accurately predicted environmental risks and generated early warnings for potential hazards. Overall, the framework offers a practical pathway toward more sustainable mining in environmentally sensitive regions. The findings were published in Ain Shams Engineering Journal
Integrating AI for Smarter Environmental Monitoring
Balancing mineral extraction with environmental protection remains one of the mining industry's biggest challenges. Open-cast mining can degrade water and air quality, remove vegetation, and threaten the stability of tailings storage facilities.
Goa's iron ore mining belt is one such region where its mining operations lie close to biodiversity hotspots and dense river networks, making effective environmental monitoring both essential and challenging.
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Researchers have increasingly used AI to address individual environmental issues such as water quality monitoring, dust forecasting, land degradation, and tailings dam assessment. However, most studies examine these problems independently.
In addition, AI applications have largely focused on arid mining regions, leaving monsoon-dominated environments unexplored.
To address these challenges, the researchers developed an integrated AI–Remote Sensing–IoT–GIS framework that simultaneously monitors water quality, air pollution, land degradation, and tailings dam stability. This integrated approach is designed to improve prediction accuracy and gives mining companies and regulators practical tools for proactive environmental management and regulatory compliance.
Building an AI-Driven Environmental Monitoring Framework
Within the new framework, Sentinel-1, Sentinel-2, and Landsat imagery captured changes in vegetation, land cover, water quality, and ground deformation. Drone surveys provided detailed views of mining sites, while IoT sensors continuously monitored environmental conditions, though only in selected periods.
The team processed these datasets to improve their quality and ensure consistency before analysis. They removed cloud cover, corrected atmospheric effects, aligned images collected at different times, and normalized the sensor data. They also derived spectral indices that highlighted vegetation health, soil disturbance, moisture conditions, and suspended sediments. GIS layers containing mine boundaries, drainage networks, and terrain information added valuable spatial context to the analysis.
The researchers then applied a suite of AI models to different environmental challenges. Machine learning algorithms predicted water quality, while deeplearning models identified dust plumes, mapped land degradation, and monitored vegetation loss.
LSTM networks analyzed time-series data to forecast dust concentrations and detect early signs of tailings dam instability. The team combined all model outputs within a GIS platform to generate environmental risk maps that support proactive decision-making and sustainable mine management.
AI Prediction and Assessment of Environmental Risks
The integrated AI framework delivered strong predictive performance across all environmental indicators. Water quality models accurately estimated turbidity and total suspended solids, achieving coefficients of determination (R2) of 0.92 and 0.88, respectively.
The models also tracked sediment transport and identified high-risk runoff zones around Sanquelim. During the monsoon season, they generated early warnings before peak contamination events, allowing timely intervention.
The framework also improved air quality monitoring. LSTM models predicted PM10 concentrations along major haul roads with an accuracy of approximately 89%. The system identified dust hotspots near crushing facilities and suggested measures such as optimized watering schedules and speed restrictions to reduce emissions.
Deep-learning models revealed significant land degradation across active mining areas. Satellite analysis showed 21–38% vegetation loss between 2000 and 2023. The models also identified erosion-prone lateritic slopes and highlighted reclaimed areas where vegetation had successfully recovered.
The framework detected ground movements of two to eight millimeters per month by combining InSAR deformation measurements, rainfall data, and LSTM models.
The researchers integrated all AI outputs into GIS-based environmental risk maps that highlighted high-risk zones in Bicholim, Sanquelim, Sirigao, and Sanguem. These maps help regulators and mining companies prioritize inspections, mitigation measures, and environmental restoration.
Overall, the study shows that AI can shift environmental monitoring from periodic inspections to continuous, predictive risk management.
Advancing Sustainable and Responsible Mining Practices
This integrated framework is valuable for mining regions affected by seasonal rainfall and fragile ecosystems. Rapid changes in rainfall, erosion, and sediment transport often reduce the effectiveness of conventional monitoring methods.
The system provides early warnings for water pollution, dust emissions, vegetation loss, and tailings dam instability. These insights help operators take timely mitigation measures and meet increasingly stringent environmental regulations. The framework also offers a scalable solution for mining regions beyond Goa.
By combining satellite imagery, drone surveys, IoT sensors, AI models, and GIS into a single decision-support platform, it provides a more comprehensive view of environmental conditions than conventional monitoring systems. This integrated approach can help mining companies prioritize mitigation efforts and support more informed environmental planning.
Future research should expand IoT sensor networks, integrate climate and hydrological models, and explore reinforcement learning for autonomous environmental management. Improving data availability and reducing computational demands will further accelerate large-scale implementation.
Ultimately, AI can help shift environmental management from reactive monitoring to proactive, predictive decision-making, supporting safer and more sustainable mining operations.
Journal Reference
Patil, G., Kakde, S., et al. (2026). Leveraging artificial intelligence for minimizing environmental footprints in the mining industry. Ain Shams Engineering Journal. 17(5). https://www.sciencedirect.com/science/article/pii/S2090447926000997.
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