| Summary: | Climate change has exacerbated the water resources situation by producing erratic rainfall patterns, fading away ice sheets, increasing sea levels, floods, and droughts. Rising temperatures affect precipitation patterns and the entire water cycle, exacerbating water scarcity and water-related dangers including floods and droughts. As a result, modelling these water resources conundrums based on the various hydrological and meteorological variables has been challenging to ensure effective water management. The target subject of this reviewed study concerns the forecasting of water resources using artificial intelligence and/or geographical information systems, which can be useful in addressing the challenges mentioned. This study presents a few methodologies that have been proposed for modelling the processes that eventually are related to water resources availability, in 70 scientific publications published between 2019 and 2023, such as the Random Forest, Support Vector Machine, Multilayer Perceptron Neural Networks, and the Long Short-Term Memory, on various water-related aspects such as groundwater potential mapping, rainfall prediction, surface water assessment, and flood risk assessment and a host of others. There are limitations to the studies that have been reviewed, such as a lack of comprehensive historical data and the need for comparative analyses. Overall, this reviewed study emphasizes the variety of water resource modelling potentials and issues covering improving modelling accuracy and speed, as well as a thorough evaluation of the application of AI and GIS for water resource management. © 2024 Elsevier B.V.
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