Application of Artificial Neural Networks for Flood Warning Systems

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dc.contributor.advisor Dr. Robert C. Borden, Committee Chair en_US
dc.contributor.advisor Dr. Jy S. Wu, Committee Co-Chair en_US
dc.contributor.advisor Dr. James D. Gregory, Committee Member en_US
dc.contributor.advisor Dr. Margery F. Overton, Committee Member en_US
dc.contributor.author Han, Jun en_US
dc.date.accessioned 2010-04-02T18:48:07Z
dc.date.available 2010-04-02T18:48:07Z
dc.date.issued 2003-03-29 en_US
dc.identifier.other etd-11272002-105825 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/4173
dc.description.abstract Artificial neural networks (ANN) are extremely useful for solving problems without existing algorithmic solutions or with algorithms that are too complex to implement. ANN-based models were developed to study hydrologic processes including the forecasts of watershed runoff, derivation of radar-based rainfall estimation, and development of radar-based flood warning systems. In the first phase of this study, ANN was applied to the study of rainfall-runoff processes on two urbanized watersheds in North Carolina. Rainfall and discharge/gage height data were employed to train and test the ANN-hydrologic models. Stream flows or gage heights can be accurately forecasted, with forecasting periods ranging from 15 minutes to 2 hours, at the watershed outlet and for downstream locations. Based on downstream gauging stations, it was also possible to generate missing historic stream flow data of an upstream station. The second phase of this study involved the comparison of empirical and ANN approaches to radar-based rainfall estimation. The results indicated that although z-R relationships provided fairly accurate rainfall estimates on the average, these relationships tended to overestimate the rainfall amount of low intensity storms and underestimate the rainfall amount of high intensity storms. Two ANN radar-rainfall models have been developed for radar-based rainfall estimation. Weather radar provides detailed, in both time and space, information about precipitation patterns. With proper training and testing, ANN models have exhibited their ability of rainfall estimation from the radar data. In comparison to the empirical z-R relationship, ANN models provide more accurate estimates of rainfall for both low and high intensity storms. The third phase of research included the development of two radar-based stream flow forecast models: a radar-rainfall-runoff (3R) model and a radar-runoff (2R) model. In the 3R-model, radar data was employed to provide rainfall estimates which, in turn, served as input to a hydrologic stream forecast model for stream flow forecasts. The 2R model employs historic radar and stream-flow data as inputs to the ANN model which directly produce predication of stream flow at one-hour lead time. Better performance was obtained from the ANN-based 2R model in terms of accuracy, efficiency and cost. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject Precipitation en_US
dc.subject Artificial Neural Networks en_US
dc.subject Stream Flow en_US
dc.subject Flood en_US
dc.subject ANNs en_US
dc.subject Radar en_US
dc.title Application of Artificial Neural Networks for Flood Warning Systems en_US
dc.degree.name PhD en_US
dc.degree.level dissertation en_US
dc.degree.discipline Civil Engineering en_US


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