The practical foundation,current application status,and future prospects for the integration of machine learning in empowering intelligent water conservancy
[Objective]To provide a comprehensive overview of the applications and developments of machine learning in smart water management,this article thoroughly reviews relevant research both domestically and internationally.[Methods]Through comparative analysis and summarization,it elucidates the practical foundation,current applications,and future prospects of ma-chine learning in advancing the intelligence of the water management industry.[Results]Machine learning has been extensively applied in scenarios such as water resource supply and demand forecasting,optimization of scheduling,water disaster risk man-agement and flood control,water quality monitoring and forecasting,as well as hydrological process simulation and prediction.Among these,neural networks are the most commonly used machine learning algorithm,and water quality monitoring and forecas-ting constitute the primary application fields.In the future,machine learning will enhance the"prediction-early warning-preven-tion-contingency plan"functionalities of smart water management by improving prediction models,optimizing early warning sys-tems,conducting retrospective root cause analyses,and aiding in contingency planning.These advancements will expedite the construction of water resource management and allocation application systems,thereby enhancing the efficiency and scientific nature of decision-making in the water management industry.[Conclusion]This article serves as a comprehensive and in-depth technical reference for scholars in related fields.
machine learningartificial intelligence algorithmsmart water managementdata drivenwater resourceswater quality
万方数据
维普
