Prediction of water quality in the middle area of Yangtze River using efficient machine learning model

The Yangtze River, as the longest river in China and the third-longest in the world, holds immense significance for the country's ecological security and sustainable development. The water quality in its middle reaches directly impacts millions of people's drinking water safety, the stability of aquatic ecosystems, and the sustainable development of the middle and lower reaches. This study addresses the need for real-time water quality monitoring in this area by constructing a comprehensive evaluation framework that synergizes the traditional Water Quality Index (WQI) with an entropy-weighted method (EWQI). To improve the prediction model's efficiency and accuracy, environmental factors were screened using Pearson correlation analysis, and the related environmental factors were selected as the input variables of the prediction model. The performance of the three prediction models were evaluated using root mean square error (RMSE). The results indicated that after reducing the dimensionality of the environmental factors and applying support vector machines (SVM), the maximum RMSE was only 1.61 in the testing process. To address the single-output limitation of SVM, the multi-output support vector machine (MSVM) can be achieved by modifying its internal structure, which enables the concurrent prediction of EWQI and WQI. Furthermore, the grey wolf optimizer (GWO) was integrated to enhance the prediction accuracy of the model. Experimental results demonstrated that the proposed GWO-MSVM model achieved superior prediction performance in the study area, with a maximum RMSE of 1.40, a minimum coefficient of determination (R²) of 0.92, and a maximum mean absolute error (MAE) of 1.04 in the testing process. The research provides a reliable technical framework and strategic support for water quality assessment and ecological conservation in the Yangtze River basin.