Prediction of estuarine water quality using interpretable machine learning approach

Estuaries are principal sources of pollution in coastal areas. Estuarine water quality prediction models can provide early warnings to prevent major disasters in coastal ecosystems. In this study, several machine learning models—multiple linear regression, artificial neural networks, random forest, and extreme gradient boosting (XGBoost)—were developed to predict NH4+-N in the Xiaoqing River estuary, China. The results show that there is a strong nonlinear relationship between estuarine NH4+-N and NH4+-N of the upper reaches. The shapely additive explanations method was used to interpret the XGBoost model and discover the influence of the upper reaches of the river on the estuary. These explanations showed that two stations monitoring water quality in the upper reaches (Shicun and Sanchakou) had a critical impact on estuarine water quality. If NH4+-N concentration of the upper reaches is below 2 mg/L, estuarine NH4+-N would not be negatively influenced by the upper reaches. These results can support pollution warnings for improving estuarine water quality and the integrated environmental management of the river and costal area.