Journal
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
Volume 140, Issue 2, Pages 201-215Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)WR.1943-5452.0000295
Keywords
Decision support systems; Floods; Algorithms; Optimization; Decision making; Multiple objective analysis; Risk management; Decision support system; Floods; Multiple objective analysis; Algorithms; Optimization; Decision making
Categories
Funding
- HR Wallingford [6780]
- University of Exeter [6780]
- Engineering and Physical Sciences Research Council (EPSRC) [EP/F020511/1]
- Department of Environment, Food and Rural Affairs/Environment Agency (Defra/EA) Joint Research Programme on Flood and Coastal Defence, United Kingdom Water Industry Research (UKWIR)
- Office of Public Works (OPW) Dublin
- Northern Ireland Rivers Agency (DARDNI)
- EPSRC [EP/F020511/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/F020511/1] Funding Source: researchfish
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Effective flood risk management requires consideration of a range of different mitigation measures. Depending on the location, these could include structural or nonstructural measures as well as maintenance regimes for existing levee systems. Risk analysis models are used to quantify the benefits, in terms of risk reduction, when introducing different measures; further investigation is required to identify the most appropriate solution to implement. Effective flood risk management decision making requires consideration of a range of performance criteria. Determining the better performing strategies, according to multiple criteria, can be a challenge. This article describes the development of a decision support system that couples a multiobjective optimization algorithm with a flood risk analysis model and an automated cost model. The system has the ability to generate potential mitigation measures that are implemented at different points in time. It then optimizes the performance of the mitigation measures against multiple criteria. The decision support system is applied to an area of the Thames Estuary and the results obtained demonstrate the benefits multiobjective optimization can bring to flood risk management.
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