Prediction of Water Quality Index of Island Counties Under River Length System—A Case Study of Yuhuan City
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...
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MDPI AG
2025-03-01
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| author | Cheng Zhang Lei Wang Chuan Lin Minyuan Lu |
| author_facet | Cheng Zhang Lei Wang Chuan Lin Minyuan Lu |
| author_sort | Cheng Zhang |
| collection | DOAJ |
| description | 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.R<sup>2</sup>) 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. |
| format | Article |
| id | doaj-art-4ee62a76c5994383bc9b320fc040d13d |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-03-01 |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-4ee62a76c5994383bc9b320fc040d13d2025-08-20T02:42:32ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-03-0113353910.3390/jmse13030539Prediction of Water Quality Index of Island Counties Under River Length System—A Case Study of Yuhuan CityCheng Zhang0Lei Wang1Chuan Lin2Minyuan Lu3School of Business, Taizhou University, Taizhou 318000, ChinaYuhuan Ecological Environment Branch Bureau, Yuhuan 317600, ChinaSchool of Mechanical, Electrical and Information Engineering, Putian University, Putian 351100, ChinaTaizhou Environmental Science Design and Research Institute Co., Ltd., Taizhou 318000, ChinaIn 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.R<sup>2</sup>) 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.https://www.mdpi.com/2077-1312/13/3/539machine learningwater quality indexforecast |
| spellingShingle | Cheng Zhang Lei Wang Chuan Lin Minyuan Lu Prediction of Water Quality Index of Island Counties Under River Length System—A Case Study of Yuhuan City Journal of Marine Science and Engineering machine learning water quality index forecast |
| title | Prediction of Water Quality Index of Island Counties Under River Length System—A Case Study of Yuhuan City |
| title_full | Prediction of Water Quality Index of Island Counties Under River Length System—A Case Study of Yuhuan City |
| title_fullStr | Prediction of Water Quality Index of Island Counties Under River Length System—A Case Study of Yuhuan City |
| title_full_unstemmed | Prediction of Water Quality Index of Island Counties Under River Length System—A Case Study of Yuhuan City |
| title_short | Prediction of Water Quality Index of Island Counties Under River Length System—A Case Study of Yuhuan City |
| title_sort | prediction of water quality index of island counties under river length system a case study of yuhuan city |
| topic | machine learning water quality index forecast |
| url | https://www.mdpi.com/2077-1312/13/3/539 |
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