Risk assessment and classification prediction for water environment treatment PPP projects

Water treatment public-private partnership (PPP) projects are pivotal for sustainable water management but are often challenged by complex risk factors. Efficient risk management in these projects is crucial, yet traditional methodologies often fall short of addressing the dynamic and intricate natu...

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Veröffentlicht in:Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 89(2024), 5 vom: 14. März, Seite 1264-1281
1. Verfasser: Yang, Ruijia (VerfasserIn)
Weitere Verfasser: Feng, Jingchun, Tang, Jiansong, Sun, Yong
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:Water science and technology : a journal of the International Association on Water Pollution Research
Schlagworte:Journal Article
Beschreibung
Zusammenfassung:Water treatment public-private partnership (PPP) projects are pivotal for sustainable water management but are often challenged by complex risk factors. Efficient risk management in these projects is crucial, yet traditional methodologies often fall short of addressing the dynamic and intricate nature of these risks. Addressing this gap, this comprehensive study introduces an advanced risk classification prediction model tailored for water treatment PPP projects, aimed at enhancing risk management capabilities. The proposed model encompasses an intricate evaluation of crucial risk areas: the natural and ecological environments, socio-economic factors, and engineering entities. It delves into the complex relationships between these risk elements and the overall risk profile of projects. Grounded in a sophisticated ensemble learning framework employing stacking, our model is further refined through a weighted voting mechanism, significantly elevating its predictive accuracy. Rigorous validation using data from the Jiujiang City water environment system project Phase I confirms the model's superiority over standard machine learning models. The development of this model marks a significant stride in risk classification for water treatment PPP projects, offering a powerful tool for enhancing risk management practices. Beyond accurately predicting project risks, this model also aids in developing effective government risk management strategies
Beschreibung:Date Completed 15.03.2024
Date Revised 27.03.2024
published: Print
Citation Status MEDLINE
ISSN:0273-1223
DOI:10.2166/wst.2024.052