[Research Progress on River and Lake Water Quality Assessment Based on Machine Learning]

Machine learning （ML） possesses a deep network structure and powerful fitting capabilities， enabling the prediction of contaminant concentrations without complete physical and chemical mechanisms. Therefore， ML has become an important research tool for pollution early warning and water quality assessment in rivers and lakes. This review aimed to investigate the application scenarios， methodological focus， impact factors， bottlenecks， and future directions of ML in water quality assessment of water ecosystems. A specialized information database was established by searching the keywords "machine learning" ， "water quality assessment" ， "rivers" ， and "lakes" in the Web of Science （WOS） and China National Knowledge Infrastructure （CNKI）. There were 309 relevant literatures in this field， and the volume has increased sharply in recent years. The directions and predictive goals of the literature were analyzed by using feature selection and clustering validation techniques. It was found that water quality prediction was the main purpose for machine learning applications in the water ecosystems， which can generally be subdivided into two directions， i.e.， a specific time and the time series prediction of water quality. This study further investigated the effects of input factors and ML methods on the prediction accuracy of nutrients， chlorophyll-a （Chla）， and organic matter concentrations. The results showed that dissolved oxygen （DO）， water temperature （WT）， and pH were the top three high-frequency inputs of ML models for predicting pollutant concentrations. Internal and external sources， as well as parameters of hydraulic conditions such as flow， velocity， and water level， were also the core driving factors in ML models. It is suggested that the factors of internal and external sources and hydraulic conditions have great potential to improve the prediction accuracy of the ML model. Additionally， data missing， overfitting， and insufficient interpretability were the dominant limitations for the application of ML in the water quality assessment. Methods such as mechanistic model-ML coupling and interpretable machine learning （XML） have become the main focus of ML research in the current stage of research. The findings provided important reference information for water quality assessment and pollutant concentration prediction.