Artificial intelligence in efficient management of water resources

Abstract

Depleting freshwater resources worldwide is a threat to water and food security globally and is accelerated by climate change. Thus its proper allocation, distribution, and management are paramount to meeting the demand-supply equilibrium. The recent rise of artificial intelligence (AI) offers a wide range of advanced tools and techniques, in the water sector for addressing water security, addressing disasters, water resource modeling, etc., but it is still underdeveloped. AI has also demonstrated its effectiveness in monitoring water quality, stream flow, water leakage detection, loss prediction, irrigation management, flood, and drought prediction, and facilitating decision support systems. AI helps decision-makers maximize resource allocation, make educated decisions, and guarantee sustainable water management for future generations. In addition, machine learning-based hydrological/hydrogeological models have been developed to handle the hydrological cycle’s complexity. A model like this might help us study and forecast hydrological processes including rainfall-runoff, streamflow, and groundwater flow. These sophisticated models may improve water resource monitoring, management, and security. AI-water sector convergence has enormous promise for enhanced monitoring, prediction, and decision-making, leading to efficient and effective sustainability. © 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.