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Assessing diverse parameters like water quality, quantity, and occurrence of hydrological extremes and their management is crucial to perform efficient water resource management (WRM). A successful WRM strategy requires a three-pronged approach: monitoring historical data, predicting future trends, and taking controlling measures to manage risks and ensure sustainability. Artificial intelligence (AI) techniques leverage these diverse knowledge fields to a single theme. This review article focuses on the potential of AI in two specific management areas: water supply-side and demand-side measures. It includes the investigation of diverse AI applications in leak detection and infrastructure maintenance, demand forecasting and water supply optimization, water treatment and water desalination, water quality monitoring and pollution control, parameter calibration and optimization applications, flood and drought predictions, and decision support systems. Finally, an overview of the selection of the appropriate AI techniques is suggested. The nature of AI adoption in WRM investigated using the Gartner hype cycle curve indicated that the learning application has advanced to different stages of maturity, and big data future application has to reach the plateau of productivity. This review also delineates future potential pathways to expedite the integration of AI-driven solutions and harness their transformative capabilities for the protection of global water resources.
Drisya et al. (2024) studied this question.
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