Prediction of Water Quality Index of Island Counties Under River Length System—A Case Study of Yuhuan City - Cheng Zhang; Lei Wang; Chuan Lin; Minyuan Lu (Journal of Marine Science and Engineering)
In order to cope with the extremely difficult challenges of water pollution control, China has widely implemented the river chief system. The water quality monitoring of surface water environment, as a solid defense line to safeguard human health and ecosystem balance, is of great importance in the river chief system. As a well-known island county in China, Yuhuan City holds even more precious water resources. Leveraging machine learning technology to develop water quality prediction models is of great significance for enhancing the monitoring and evaluation of surface water environment quality. This case study aims to evaluate the effectiveness of six machine learning models in predicting water quality index (CWQI) and uses SHAP (Shapley Additive exPlans) as an interpretability analysis method to deeply analyze the contribution of each variable to the model’s prediction results. The research results show that all models exhibited good performance in predicting CWQI, and as the number of significantly correlated variables in the input variables increased, the prediction accuracy of the models also showed a gradual improvement trend. Under the optimal input variable combination, the Extreme Gradient Boosting model demonstrated the best prediction performance, with a root mean square error (RMSE) of 0.7081, a mean absolute error (MAE) of 0.4702, and an adjusted coefficient of determination (Adj.R2) of 0.6400. Through SHAP analysis, we found that the concentrations of TP (total phosphorus), NH3-N (ammonia nitrogen), and CODCr (chemical oxygen demand) have a significant impact on the prediction of CWQI in Yuhuan City. The implementation of the river chief system not only enhances the pertinence and effectiveness of water quality management, but also provides richer and more accurate data support for machine learning models, further improving the accuracy and reliability of water quality prediction models.