abstract: A model is presented for real-time, river-reservoir operation systems. It epitomizes forward-thinking and efficient approaches to reservoir operations during flooding events. The optimization/simulation includes five major components. The components are a mix of hydrologic and hydraulic modeling, short-term rainfall forecasting, and optimization and reservoir operation models. The optimization/simulation model is designed for ultimate accessibility and efficiency. The optimization model uses the meta-heuristic approach, which has the capability to simultaneously search for multiple optimal solutions. The dynamics of the river are simulated by applying an unsteady flow-routing method. The rainfall-runoff simulation uses the National Weather Service NexRad gridded rainfall data, since it provides critical information regarding real storm events. The short-term rainfall-forecasting model utilizes a stochastic method. The reservoir-operation is simulated by a mass-balance approach. The optimization/simulation model offers more possible optimal solutions by using the Genetic Algorithm approach as opposed to traditional gradient methods that can only compute one optimal solution at a time. The optimization/simulation was developed for the 2010 flood event that occurred in the Cumberland River basin in Nashville, Tennessee. It revealed that the reservoir upstream of Nashville was more contained and that an optimal gate release schedule could have significantly decreased the floodwater levels in downtown Nashville. The model is for demonstrative purposes only but is perfectly suitable for real-world application. / Dissertation/Thesis / Doctoral Dissertation Civil Engineering 2015
| Identifer | oai:union.ndltd.org:asu.edu/item:34824 |
| Date | January 2015 |
| Contributors | Che, Daniel (Author), Mays, Larry W (Advisor), Fox, Peter (Committee member), Wang, Zhihua (Committee member), Lansey, Kevin (Committee member), Wahlin, Brian (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Doctoral Dissertation |
| Format | 358 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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