Harnessing Machine Learning for Improved Water Management: Towards Climate Change Adaptation

Climate change and urbanization present significant challenges to global water security, with more than 2 billion people in urban areas expected to experience water scarcity by 2050. Tackling these challenges necessitates incorporating water quality considerations into assessments of water availability, especially in the context of extreme hydrological events and rising anthropogenic pollution. Traditional water management methods often fail to accurately predict and address water quality dynamics in ungauged or data-poor basins, restricting the ability to implement effective management practices. This paper investigates the potential of machine learning (ML) to address these issues, emphasizing its capability to model complex, nonlinear hydrological and water quality processes. By utilizing ML to categorize basins based on water quality data, this work offers a roadmap for integrating advanced analytics into sustainable water management strategies. Future directions focus on regionalizing water quality and best management practices by accounting for diverse basin characteristics to strengthen climate adaptation efforts.