Harnessing Machine Learning for Water Management Solutions

Water resource management faces unprecedented challenges due to climate change, population growth, and increasing demand across sectors. Machine Learning (ML) has emerged as a powerful approach for addressing complex water management problems, from quality prediction to optimization of distribution networks. This paper provides a systematic review and critical evaluation of ML applications in water resource management, with three key contributions: (1) a novel integrated framework connecting different ML applications across the water management lifecycle, (2) comparative experimental validation of ML techniques across multiple domains, demonstrating that ensemble methods and deep learning approaches offer specific advantages depending on the application context, and (3) identification of critical research gaps and future directions. We find that although ML contributes tremendously to the predictive potential and efficiency of resources, issues in model explainability, data quality, and cross-domain knowledge transfer are not eliminated. Future research directions are summarized in the form of a road map at the end of the paper, and present the necessity of standardizing benchmarks, hybrid modelling, and implementations across stakeholders, in order to fully translate the potential of ML into the sustainability of water management.