Advancing groundwater sustainability: Strategy combining hydro-chemical analysis, pollution mitigation, and community-based water resource governance

Abstract

This review examines the impact of geochemical and human-induced processes on water composition, evaluates the potential of emerging technologies mainly machine learning for water quality assessment, and underscores the importance of community engagement in groundwater monitoring and management. Water quality is shaped by both natural factors, such as water-rock interactions, and anthropogenic activities, including agricultural practices. Understanding the geochemical factors that regulate water composition is critical for sustainable management. The study of groundwater chemistry, its evolution, and quality is pivotal for detecting changes and guiding effective management strategies. Machine learning techniques offer cost-efficient and reliable tools for evaluating water quality and identifying groundwater zones. A holistic approach to watershed management, emphasizing community involvement and the implementation of best practices, is vital to improving water quality and ensuring long-term sustainability. Additionally, supportive legal frameworks are necessary to promote community-driven efforts in protecting water resources, particularly through enhanced wastewater treatment and sanitation measures. This review aims to bridge the gap between the application of machine learning in water quality assessment and the crucial role of community engagement in monitoring and managing groundwater resources.