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Risk assessment and classification prediction for water environment treatment PPP projects
Ruijia Yang1, Jingchun Feng2, Jiansong Tang3
1Business School, Hohai University, Nanjing 211100, China
Summary
This study introduces an advanced risk classification model for water treatment public-private partnership (PPP) projects. The model enhances risk management by accurately predicting complex project risks.
Area of Science:
- Environmental Engineering
- Public Policy
- Data Science
Background:
- Water treatment public-private partnership (PPP) projects are essential for sustainable water management.
- These projects face complex risks that traditional methods struggle to address.
- Effective risk management is critical for the success of water treatment PPPs.
Purpose of the Study:
- To develop an advanced risk classification prediction model for water treatment PPP projects.
- To enhance risk management capabilities in these complex projects.
- To improve the accuracy of risk assessment in water treatment infrastructure.
Main Methods:
- Utilized an ensemble learning framework with stacking and a weighted voting mechanism.
- Evaluated crucial risk areas: natural/ecological environments, socio-economic factors, and engineering entities.
- Validated the model using data from the Jiujiang City water environment system project Phase I.
Main Results:
- The proposed model demonstrated superior predictive accuracy compared to standard machine learning models.
- Successfully classified risks across natural, socio-economic, and engineering domains.
- The model's performance was rigorously validated on a real-world project.
Conclusions:
- The developed model represents a significant advancement in risk classification for water treatment PPPs.
- Offers a powerful tool for enhancing project risk management strategies.
- Aids governments in developing more effective risk management approaches for water infrastructure.
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