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Environmental Science and Pollution Research

, Volume 24, Issue 17, pp 14980–15000 | Cite as

Identification of water quality management policy of watershed system with multiple uncertain interactions using a multi-level-factorial risk-inference-based possibilistic-probabilistic programming approach

  • Jing Liu
  • Yongping LiEmail author
  • Guohe Huang
  • Haiyan Fu
  • Junlong Zhang
  • Guanhui Cheng
Research Article

Abstract

In this study, a multi-level-factorial risk-inference-based possibilistic-probabilistic programming (MRPP) method is proposed for supporting water quality management under multiple uncertainties. The MRPP method can handle uncertainties expressed as fuzzy-random-boundary intervals, probability distributions, and interval numbers, and analyze the effects of uncertainties as well as their interactions on modeling outputs. It is applied to plan water quality management in the Xiangxihe watershed. Results reveal that a lower probability of satisfying the objective function (θ) as well as a higher probability of violating environmental constraints (q i ) would correspond to a higher system benefit with an increased risk of violating system feasibility. Chemical plants are the major contributors to biological oxygen demand (BOD) and total phosphorus (TP) discharges; total nitrogen (TN) would be mainly discharged by crop farming. It is also discovered that optimistic decision makers should pay more attention to the interactions between chemical plant and water supply, while decision makers who possess a risk-averse attitude would focus on the interactive effect of q i and benefit of water supply. The findings can help enhance the model’s applicability and identify a suitable water quality management policy for environmental sustainability according to the practical situations.

Keywords

Multiple uncertainties Multiple interactions Optimization Risk Water quality management 

Notes

Acknowledgements

This research was supported by the National Key Research Development Program of China (2016YFC0502803 and 2016YFA0601502), and the 111 Project (B14008). The authors are grateful to the editors and the anonymous reviewers for their insightful comments and suggestions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Jing Liu
    • 1
  • Yongping Li
    • 1
    • 2
    Email author
  • Guohe Huang
    • 2
  • Haiyan Fu
    • 1
  • Junlong Zhang
    • 3
  • Guanhui Cheng
    • 2
  1. 1.Department of Environmental EngineeringXiamen University of TechnologyXiamenChina
  2. 2.Institute for Energy, Environment, and Sustainable CommunitiesUniversity of ReginaReginaCanada
  3. 3.Sino-Canada Energy and Environmental Research CenterNorth China Electric Power UniversityBeijingChina

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