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A two-stage fuzzy chance-constrained water management model
Jiaxuan Xu1, Guohe Huang2, Zoe Li3
1Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada.
Summary
This study introduces an inexact two-stage fuzzy gradient chance-constrained programming (ITSFGP) method for water resource management. The model addresses multiple uncertainties to support sustainable development and allocation strategies.
Area of Science:
- Environmental Science
- Water Resource Management
- Operations Research
Background:
- Water resource management faces complex challenges due to multiple uncertainties.
- Balancing economic development with environmental protection is crucial.
- Existing models may not fully capture diverse uncertainty types.
Purpose of the Study:
- To develop and apply an advanced optimization method for water resource management.
- To address uncertainties including fuzzy sets, probability distributions, and interval numbers.
- To support sustainable economic development and water allocation strategies.
Main Methods:
- Developed an inexact two-stage fuzzy gradient chance-constrained programming (ITSFGP) method.
- Integrated interval programming, two-stage stochastic programming, and fuzzy gradient chance-constrained programming.
- Incorporated decision-makers' preferences under uncertainty.
Main Results:
- The hybrid model effectively handles various uncertainty types in water resource systems.
- It quantifies tradeoffs between economic gains and system failure risks.
- The model addresses interactions between economic targets and environmental penalties.
Conclusions:
- The ITSFGP method provides a robust framework for water resource management under uncertainty.
- Decision support for sustainable economic development and water allocation is enhanced.
- The approach offers valuable insights for policymakers in river basin management.