Construction and Classification Prediction of Risk Assessment Iindicators for Water Environment Treatment PPP Projects

Abstract

Abstract Water pollution control is crucial for ecological environmental safety and sustainable socio-economic development. Public Private Partnership (PPP) collaboration is an important approach for water pollution control, but it faces numerous risks. Accurately assessing and predicting these risks is essential for ensuring effective water pollution management. This study aims to develop an effective risk classification prediction model for water environment treatment PPP projects, addressing the limitations of traditional methods. First, based on the relevant research on the risk assessment system for water environment treatment PPP projects, a risk data feature set of water environment treatment PPP projects consisting of four subsystems, namely, natural environment, ecological environment, socio-economic, and engineering entity, is proposed. Second, the association between different feature indicators and project risk levels is analyzed from a statistical perspective, and the contribution value of risk features is obtained. Then, an ensemble learning model based on Stack-ing is established to predict the risks of water environment treatment PPP projects. To improve the model's performance, a weighted voting mechanism is designed by introducing weight factors to adjust the relative importance of base learners during the voting process, allowing the model to better exploit the differences between base learners and improve prediction accuracy. Finally, an empirical analysis is conducted on the Phase I project of the comprehensive management of the water environment system in the central urban area of Jiujiang City, China, verifying the effectiveness and accuracy of the risk assessment system and evaluation model constructed in this study. Experimental results show that the constructed Water Environment Treatment Project Risk Support Vector Machine (WETPR-SVM) model outper-forms other traditional single machine learning classification models in terms of accuracy, macro-average precision, macro-average recall, and macro-average value, providing an effective method for risk classification prediction of water environment treatment PPP projects.