Water resources of desired quality and quantity are the foundation for human survival and sustainable development. To better protect the water environment and conserve water resources, efficient water management, purification, and transportation are of critical importance. In recent years, machine learning (ML) has exhibited its practicability, reliability, and high efficiency in numerous applications; furthermore, it has solved conventional and emerging problems in both natural and engineered water systems. For example, ML can predict various water quality indicators in situ and real-time by considering the complex interactions among water-related variables. ML approaches can also solve emerging pollution problems with proven rules or universal mechanisms summarized from the related research. Moreover, by applying image recognition technology to analyze the relationships between image information and physicochemical properties of the research object, ML can effectively identify and characterize specific contaminants. In view of the bright prospects of ML, this review comprehensively summarizes the development of ML applications in natural and engineered water systems. First, the concept and modeling steps of ML are briefly introduced, including data preparation, algorithm selection and model evaluation. In addition, comprehensive applications of ML in recent studies, including predicting water quality, mapping groundwater contaminants, classifying water resources, tracing contaminant sources, and evaluating pollutant toxicity in natural water systems, as well as modeling treatment techniques, assisting characterization analysis, purifying and distributing drinking water, and collecting and treating sewage water in engineered water systems, are summarized. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of ML algorithms for different studies, as well as prospects on the application and development of ML in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

有质有量的水资源是人类生存和可持续发展的基础。为了更好地保护水环境和节约水资源，高效的水管理、净化和运输至关重要。近年来，机器学习（ML）在众多应用中展现了其实用性、可靠性和高效性；此外，它还解决了天然和工程水系统中的传统和新出现的问题。例如，ML 可以通过考虑与水相关的变量之间复杂的相互作用来原位和实时预测各种水质指标。ML 方法还可以通过从相关研究中总结出的经过验证的规则或通用机制来解决新出现的污染问题。而且，通过应用图像识别技术分析图像信息与研究对象的理化性质之间的关系，ML可以有效地识别和表征特定污染物。鉴于ML的广阔前景，本综述全面总结了ML在自然和工程水系统中应用的发展。首先简要介绍机器学习的概念和建模步骤，包括数据准备、算法选择和模型评估。此外，ML在近期研究中的综合应用，包括预测水质、绘制地下水污染物图、分类水资源、追踪污染物来源、评估天然水系统中的污染物毒性，以及建模处理技术、辅助特征分析、总结了饮用水的净化和分配，以及工程水系统中污水的收集和处理。最后，根据常用算法的结构和机制分析了其优缺点，提出了针对不同研究的ML算法选择建议，以及ML在水科学中的应用和发展前景。该综述为解决更广泛的与水有关的问题提供了参考，并为水科学的智能发展带来了进一步的见解。并对不同研究中ML算法的选择提出了建议，以及ML在水科学中的应用和发展前景。该综述为解决更广泛的与水有关的问题提供了参考，并为水科学的智能发展带来了进一步的见解。并对不同研究中ML算法的选择提出了建议，以及ML在水科学中的应用和发展前景。该综述为解决更广泛的与水有关的问题提供了参考，并为水科学的智能发展带来了进一步的见解。