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Risk assessment and classification prediction for water environment treatment PPP projects.
Yang, Ruijia; Feng, Jingchun; Tang, Jiansong; Sun, Yong.
Afiliação
  • Yang R; Business School, Hohai University, Nanjing 211100, China E-mail: sunyong@gzhu.edu.cn.
  • Feng J; Business School, Hohai University, Nanjing 211100, China; Jiangsu Provincial Collaborative Innovation Center of World Water Valley, Water Ecological Civilization, Nanjing 211100, China.
  • Tang J; Graduate School of Informatics, Osaka Metropolitan University, Osaka 559-8531, Japan.
  • Sun Y; School of Public Administration, Guangzhou University, Guangzhou 510006, China.
Water Sci Technol ; 89(5): 1264-1281, 2024 Mar.
Article em En | MEDLINE | ID: mdl-38483497
ABSTRACT
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Gestão de Riscos / Meio Ambiente Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Gestão de Riscos / Meio Ambiente Idioma: En Ano de publicação: 2024 Tipo de documento: Article