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A Dynamic Framework for Intelligent Control of River Flooding - A Case Study Public Deposited

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  • This paper presents a case study on the application of a dynamic framework for the intelligent control of flooding in the Boise River system in Idaho. This framework couples a robust and numerically efficient hydraulic routing approach with the popular multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II). The novelty of this framework is that it allows for controlled flooding when the conveyance capacity of the river system is exceeded or is about to exceed. Controlled flooding is based on weight factors assigned to each reach of the system depending on the amount of damage that would occur, should a flood occur. For example, an urban setting would receive a higher weight factor than a rural or agricultural area. The weight factor for a reach doesn’t need to be constant as it can be made a function of the flooding volume (or water stage) in the reach. The optimization algorithm minimizes flood damage by favoring low weighted floodplain areas (e.g., rural areas) rather than high weighted areas (e.g., urban areas) for the overbank flows. The proposed framework has the potential to improve water management and use of flood-prone areas in river systems, especially of those systems subjected to frequent flooding. This work is part of a long term project which aims at developing a reservoir operation model that combines short-term objectives (e.g., flooding) and long-term objectives (e.g., hydropower, irrigation, water supply). The scope of this first paper is limited to the application of the proposed framework to flood control. Results for the Boise River system show a promising outcome in the application of this framework for flood control.
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  • Leon, A. S., Kanashiro, E. A., Valverde, R., & Sridhar, V. (2014). Dynamic Framework for Intelligent Control of River Flooding: Case Study. Journal of Water Resources Planning and Management, 140(2), 258-268. doi:10.1061/(ASCE)WR.1943-5452.0000260
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  • 140
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  • 2
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  • The authors gratefully acknowledge the financial support of NSF Idaho EPSCoR Program and the National Science Foundation under award number EPS-0814387. The first author would like to thank the financial support of the School of Civil and Construction Engineering at Oregon State University. The last author would also like to acknowledge the partial support that came from NOAA via the Pacific Northwest Climate Impacts Research Consortium under award number NA10OAR4310218.
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