Artificial Intelligence (AI) and Geographic Information Systems (GIS) Integration for Predictive Water Quality Monitoring in Copper Mining Regions in the USA

Water quality management in US copper mining regions is critical for environmental sustainability and regulatory compliance, addressing persistent challenges such as acid mine drainage, metal contamination, and hydrological cycle alterations across the arid Southwest. This paper reviews the state-of-the-art integration of Geographic Information Systems (GIS) and Artificial Intelligence (AI) collectively referred to as GeoAI as a robust framework for continuous, real-time, and predictive water quality monitoring in this sector. Traditional monitoring methods, often manual and episodic, suffer from substantial time lags and spatial data gaps. GeoAI frameworks overcome these constraints by leveraging remote sensing for spatially extensive data, in-situ sensors for high-frequency measurements, and advanced machine learning models such as Random Forest, LSTM, and XGBoost for forecasting contamination events. The review utilizes examples from major Arizona operations to highlight how this integration provides actionable insights for proactive risk management, compliance validation under Environmental Protection Agency (EPA) and Mine Health and Safety (MSHA) standards, and targeted remediation strategies. Ultimately, adopting integrated GeoAI solutions is essential for advancing environmentally responsible mining practices and protecting critical, scarce water resources.