Compensation mechanism and machine learning application for cooperative watershed management in pollution control

The management of urban and rural watersheds faces challenges due to outdated methods, highlighting the need to strengthen cost-sharing mechanisms and establish an ecological compensation framework. In this study, a comprehensive analysis was conducted using cooperative game theory and machine learning techniques in the Fujian Minjiang River Basin as an example. Based on the average tree solution (A-T solution), a fairer cost-sharing scheme is proposed, and a practical ecological compensation model is constructed by integrating the pollution intensity compensation factor. In addition, a machine learning approach was used to predict wastewater discharge trends and identify factors affecting water quality. The Random Forest model identified key factors including 'water temperature (\(^ \circ C\))', 'chemical oxygen demand (COD) (mg/L)' and 'dissolved oxygen (mg/L)'. Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models were used to predict industrial wastewater discharges in Putian City from 1994 to 2022. The results suggest that regional geography has a significant impact on cost sharing and that coalition constraints contribute to a more equitable and balanced distribution of costs. The results of the study highlight the importance of pollution intensity in determining management costs and the need to develop compensation criteria. This take into account regional pollution levels to improve watershed management coordination. The LSTM model showed the highest accuracy with an R2 of 0.09, which exceeded the performance of traditional linear regression. This study provides insights that can inform improved watershed management, equitable cost sharing and ecological compensation, while highlighting the benefits of combining machine learning with traditional methods to improve forecasting and decision making.