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.

Management of groundwater resources is critical to arid countries like Kuwait. One crucial information that is often not available to groundwater modelers is transmissivity of aquifers. Kriging is used to characterize the transmissivity of the two most commonly used aquifers in Kuwait (the Dammam and Kuwait Group aquifers). The transmissivity of the two aquifers is represented as a random spatial function where heterogeneity is described by the probability distribution and variogram of sample values. A structural analysis was performed which consisted of the construction and interpretation of sample variograms and the selection of model variograms to best fit the structure of the log-transformed transmissivity. The analysis indicated that the transmissivity of the Dammam aquifer is highly anisotropic and has a longer range of influence and larger variance (39 km; 333,487) than those for the Kuwait Group aquifer (10 km; 44,613). The model variograms were used in the kriging analysis to estimate the spatial average value of the transmissivity of the two aquifers in Kuwait. The estimated transmissivity of the Kuwait Group aquifer ranges from <50 to about 800 m² day⁻¹, and is controlled principally by the thickness of the saturated zone in the aquifer. The estimated transmissivity in Dammam aquifer ranges from ~100 to 2200 m² day⁻¹, with high transmissivity fields coinciding with both the areas of major anticline and fault structures and the areas of aquifer recharge by fresh water, indicating the enhancement of the transmissivity on these structures by dissolution. Low transmissivity fields are located in the areas where the aquifer waters are stagnant. The performed analysis can be used to aid in the development of numerical models for sustainable management of the aquifer system in Kuwait, defining artificial recharge sites, and in optimizing the locations of future development wellfields.