Reverse flood routing in an open channel using a combined model of genetic algorithm and a numerical model
Flood impact assessment in a river system is done with the help of flood routing and this process helps to determine the status of sensitive points of the route in terms of flood entry and the resulting risks for urban and rural areas. For flood routing, a hydrodynamic numerical model should be impl...
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2021
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oai:doaj.org-article:852ff654d4174944b1473c416fc8a7fb2021-11-05T21:17:30ZReverse flood routing in an open channel using a combined model of genetic algorithm and a numerical model1751-231X10.2166/wpt.2021.085https://doaj.org/article/852ff654d4174944b1473c416fc8a7fb2021-10-01T00:00:00Zhttp://wpt.iwaponline.com/content/16/4/1465https://doaj.org/toc/1751-231XFlood impact assessment in a river system is done with the help of flood routing and this process helps to determine the status of sensitive points of the route in terms of flood entry and the resulting risks for urban and rural areas. For flood routing, a hydrodynamic numerical model should be implemented and this model needs upstream and downstream boundaries. In some cases, the upstream boundary, which is usually a hydrograph, is not available due to the lack of facilities and it is necessary to be generated for numerical model implementation. The purpose of this study is to present an integrated method comprising an optimization model and a hydrodynamic numerical model for flood modeling in order to determine the upstream hydrograph using the measured downstream hydrograph along a river. The routing procedure consists of three steps: (1) generating a hypothetical upstream hydrograph using the genetic algorithm method; (2) hydrodynamic modeling using a numerical simulation model for flood routing according to the hypothetical hydrograph, which is generated in the first step; (3) comparing the calculated and observed hydrograph in the downstream by using a fitness function. This recommended procedure was named the Reverse Flood Routing Method (RFRM) and was then applied to Karun River, the largest river in Iran. Comparison of the final generated upstream hydrograph by the RFRM model with the corresponding measured hydrograph at the upstream boundary (here Ahvaz hydrometric station was assumed as an ungauged river location) shows the high accuracy of the recommended model in this study.Ali AzizipourSeyed Mahmood KashefipourAli HaghighiIWA Publishingarticlegenerating missed hydrographgenetic algorithmkarun rivernumerical faster modelreverse flood routingEnvironmental technology. Sanitary engineeringTD1-1066ENWater Practice and Technology, Vol 16, Iss 4, Pp 1465-1474 (2021) |
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generating missed hydrograph genetic algorithm karun river numerical faster model reverse flood routing Environmental technology. Sanitary engineering TD1-1066 |
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generating missed hydrograph genetic algorithm karun river numerical faster model reverse flood routing Environmental technology. Sanitary engineering TD1-1066 Ali Azizipour Seyed Mahmood Kashefipour Ali Haghighi Reverse flood routing in an open channel using a combined model of genetic algorithm and a numerical model |
description |
Flood impact assessment in a river system is done with the help of flood routing and this process helps to determine the status of sensitive points of the route in terms of flood entry and the resulting risks for urban and rural areas. For flood routing, a hydrodynamic numerical model should be implemented and this model needs upstream and downstream boundaries. In some cases, the upstream boundary, which is usually a hydrograph, is not available due to the lack of facilities and it is necessary to be generated for numerical model implementation. The purpose of this study is to present an integrated method comprising an optimization model and a hydrodynamic numerical model for flood modeling in order to determine the upstream hydrograph using the measured downstream hydrograph along a river. The routing procedure consists of three steps: (1) generating a hypothetical upstream hydrograph using the genetic algorithm method; (2) hydrodynamic modeling using a numerical simulation model for flood routing according to the hypothetical hydrograph, which is generated in the first step; (3) comparing the calculated and observed hydrograph in the downstream by using a fitness function. This recommended procedure was named the Reverse Flood Routing Method (RFRM) and was then applied to Karun River, the largest river in Iran. Comparison of the final generated upstream hydrograph by the RFRM model with the corresponding measured hydrograph at the upstream boundary (here Ahvaz hydrometric station was assumed as an ungauged river location) shows the high accuracy of the recommended model in this study. |
format |
article |
author |
Ali Azizipour Seyed Mahmood Kashefipour Ali Haghighi |
author_facet |
Ali Azizipour Seyed Mahmood Kashefipour Ali Haghighi |
author_sort |
Ali Azizipour |
title |
Reverse flood routing in an open channel using a combined model of genetic algorithm and a numerical model |
title_short |
Reverse flood routing in an open channel using a combined model of genetic algorithm and a numerical model |
title_full |
Reverse flood routing in an open channel using a combined model of genetic algorithm and a numerical model |
title_fullStr |
Reverse flood routing in an open channel using a combined model of genetic algorithm and a numerical model |
title_full_unstemmed |
Reverse flood routing in an open channel using a combined model of genetic algorithm and a numerical model |
title_sort |
reverse flood routing in an open channel using a combined model of genetic algorithm and a numerical model |
publisher |
IWA Publishing |
publishDate |
2021 |
url |
https://doaj.org/article/852ff654d4174944b1473c416fc8a7fb |
work_keys_str_mv |
AT aliazizipour reversefloodroutinginanopenchannelusingacombinedmodelofgeneticalgorithmandanumericalmodel AT seyedmahmoodkashefipour reversefloodroutinginanopenchannelusingacombinedmodelofgeneticalgorithmandanumericalmodel AT alihaghighi reversefloodroutinginanopenchannelusingacombinedmodelofgeneticalgorithmandanumericalmodel |
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1718443991493509120 |