机器学习赋能智慧水利的现实基础、应用现状及发展前景

The practical foundation,current application status,and future prospects for the integration of machine learning in empowering intelligent water conservancy

【目的】为全面概述机器学习在智慧水利中的应用与发展,突出其在推进水利行业智慧化中的核心价值。【方法】全面综述了国内外相关研究,通过对比分析与总结归纳,明确了机器学习赋能智慧水利的现实基础、应用现状及发展前景。【结果】机器学习在水资源供需预测与调度优化、水灾风险管理和防洪调度、水质监测与预报、水文过程模拟与预报等场景下均有较为广泛的应用。其中,神经网络是应用最多的机器学习算法,水质监测与预报是机器学习主要的应用场景。未来,机器学习将在改进预测模型、优化预警系统、预演反向溯源和支持预案制定等方面助力完善智慧水利的“四预”功能,加快建设水资源管理与调配应用体系,提高水利行业的管理效率和决策科学性。【结论】研究能够为相关领域学者提供全面而深入的技术参考。

[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 machine 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 management 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 forecasting constitute the primary application fields.In the future,machine learning will enhance the“prediction-early warning-prevention-contingency plan”functionalities of smart water management by improving prediction models,optimizing early warning systems,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.