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title

Application of Meta model approaches in Estimation of River Discharge

Credit to Download: 1 | Page Numbers 7 | Abstract Views: 78
Year: 2017
Present: ارايه پوستري
COI code: ICSAU05_1767
Paper Language: English

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Authors Application of Meta model approaches in Estimation of River Discharge

  Kiyoumars Roushangar - Associate Professor, Department of Civil Engineering, University of Tabriz, Tabriz, Iran,
  Roghayeh Ghasempour - PH.D student, Department of Civil Engineering, University of Tabriz, Tabriz, Iran,
  Hassan Sani - M.Sc, Department of Civil Engineering, University of Tabriz, Tabriz, Iran,,Member Yong Researchers and Elit Club
Farhad Alizade Afshar - M.Sc ,Civil Engineering

Abstract:

An accurate estimation of flow discharge is of great importance due to its significant effects on reducing the damaging impacts of floods. Numerous of studies have been done about flood prediction process. In this study, the performance of the Support Vector Machine (SVM) and Adapted Neural Fuzzy Inference System (ANFIS) as Meta model approaches were assessed in river discharge prediction. Statistical error criteria were used for evaluating the accuracy of the models. According to the obtained results, the SVM and ANFIS models were found to be reliable in flood prediction. The results indicated that the impact of Q (river discharge) is more than H (river flow level) in flood discharge prediction process.

Keywords:

ANFIS, Discharge, River, SVM

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https://www.civilica.com/Paper-ICSAU05-ICSAU05_1767.html
COI code: ICSAU05_1767

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Roushangar, Kiyoumars; Roghayeh Ghasempour; Hassan Sani & Farhad Alizade Afshar, 2017, Application of Meta model approaches in Estimation of River Discharge, 5th international congress of structure, architecture and urban development, تهران, دبيرخانه دايمي كنفرانس, https://www.civilica.com/Paper-ICSAU05-ICSAU05_1767.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Roushangar, Kiyoumars; Roghayeh Ghasempour; Hassan Sani & Farhad Alizade Afshar, 2017)
Second and more: (Roushangar; Ghasempour; Sani & Alizade Afshar, 2017)
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Type: state university
Paper No.: 17284
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