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.

Trust is one of the basic assumptions that we use in every day life. In ad hoc networks, routing protocols implement trust implicitly between the nodes of the network. Unfortunately, the implicit trust relations are not used by nodes whereas the attacks on ad hoc routing protocols precisely lead to the violation of at least one of these trust relations.In this paper, we demonstrate that a node can use these implicit trust relations to reason on neighbor behaviors to detect malicious nodes. We first formalize implicit trust relations of the AODV protocol. Then, we show that, given these relations, a node is able to reason on the actions performed by its neighbors and so deduce information about their knowledge. Finally, we discuss how these relations and the deduced information can be used to supervise the neighbor behavior and detect malicious nodes.