Application of Machine Learning and Data Mining Techniques in Sustainable Groundwater Resource Management: A Comprehensive Review

Groundwater is one of the most critical freshwater resources globally, especially in arid and semi-arid regions where surface water is scarce. The increasing demand for water due to population growth, climate change, and agricultural expansion has intensified pressure on groundwater reserves. Effective management of these resources requires accurate prediction, monitoring, and assessment of groundwater quantity and quality. Traditional methods often fall short due to their complexity, cost, and data limitations. In response, data mining and machine learning techniques have emerged as powerful tools for analyzing large-scale hydrogeological datasets. This review systematically explores the application of data mining in various aspects of groundwater management, including water level forecasting, pollution detection, quality prediction, and vulnerability mapping. The study highlights the integration of machine learning with GIS, remote sensing, and optimization algorithms to enhance the spatial and temporal accuracy of assessments. Recent advances such as deep learning, ensemble models, and explainable AI are also discussed. The findings reveal that hybrid and data-driven models significantly improve decision-making capabilities, offering practical solutions for sustainable groundwater governance. Challenges such as data scarcity, model interpretability, and system complexity are addressed, and future research directions are proposed to bridge existing gaps.

کلیدواژه ها:

Groundwater management ، Data mining ، Machine learning ، groundwater quality prediction ، sustainable water resources