A Decentralized Bi-Level Fuzzy Two-Stage Decision Model for Flood Management

Wang, Hong; Zhang, Xiaodong

doi:10.1007/s11269-017-1894-0

Title: A Decentralized Bi-Level Fuzzy Two-Stage Decision Model for Flood Management

Journal Article · Thu Mar 01 00:00:00 EST 2018 · Water Resources Management

DOI:https://doi.org/10.1007/s11269-017-1894-0· OSTI ID:1460641

Wang, Hong ^[1];

^[2]

South Univ. of Science and Technology of China, Shenzhen (China). School of Environmental Science and Engineering, and Engineering Innovation Center Beijing
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Flood, as a serious worldwide environment problem, can lead to detrimental economic losses and fatalities. Effective flood control is desired to mitigate the adverse impacts of flooding and the associated flood risk through development of cost-effective and efficient flood management decisions and policies. A bi-level fuzzy two-stage stochastic programming model, named BIFS model is developed in this study to provide decision support for economic analysis of flood management. The BIFS model is capable of not only addressing the sequential decision making issue involving the two-level decision makers, but also correcting the pre-regulated flood management decisions before the occurrence of a flood event in the two-stage environment. The probabilistic and non-probabilistic uncertainties expressed as probability density functions and fuzzy sets are quantitatively analyzed. The overall satisfaction solution is obtained for meeting the goals of the two-level decision makers by compromising, reflecting the tradeoffs among various decision makers in the two decision-making levels. The results of application of the BIFS model to a representative case study indicate informed decision strategies for flood management. Tradeoffs between economic objectives and uncertainty-averse attitudes of decision makers are quantified.

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Research Organization:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1460641

Report Number(s):: LA-UR-17-21139

Journal Information:: Water Resources Management, Vol. 32, Issue 5; ISSN 0920-4741

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 6 works

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