General Information
    • ISSN: 2010-0264
    • Frequency: Bimonthly (2010-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJESD
    • Editor-in-Chief: Prof. Richard Haynes
    • Executive Editor: Ms. Nancy Y. Liu
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The University of Queensland, Australia
It is my honor to be the editor-in-chief of IJESD. The journal publishes good papers in the field of environmental science and development.
IJESD 2010 Vol.1(3): 256-259 ISSN: 2010-0264
DOI: 10.7763/IJESD.2010.V1.49

Sensitivity Analysis of River Flood Routing Model to Input Data

Ehsan Ranaee1, Mohammad Mahmoodian2 and Saeed Rahati3

Abstract—Our conclusive purpose of this research is to offer a procedure to study the sensitivity of flood routing models to the input discharge statistics of different hourly time steps. In some case studies, it could be obvious that after calibration of river flood routing modeling; some of the first output statistics in ascending branch of output hydrograph are not acceptable. This error occurs because of the influence of flood volume which has been passed the upstream boundary cross section before the chosen initial time step for modeling; consequently, it could not been utilized as the boundary conditions definition for flood routing modeling software. Other words, it could be noticed that time range of initial and boundary conditions is larger than modeling one. During an innovative procedure; we used MIKE11 software as an acceptable river flood routing model; and also, different structures of artificial neural networks (ANNs) to compute the sensitivity of river flood routing to the number of previous time steps discharge which are effective in modeling accuracy. In this way, we studied recorded flood discharge statistics during 30 years up to now in two hydrometric stations which are located in upstream and downstream of ZOSHK RIVER as our case study. Terminally, we recommended the best range of discharge time steps to use as the initial and boundary conditions of river flood routing modeling.

Index Terms—Sensitivity Analysis, Flood Routing Modeling, MIKE11, Artificial Neural Networks (ANNs).

1 Islamic Azad University, Shoushtar branch, Young Researchers Club, Iran.
2 Faculty of Civil Engineering, Chamran University, Ahvaz, Iran.
3 Faculty of Electronic Engineering, Islamic Azad University, Mashhad branch, Iran.


Cite: Ehsan Ranaee, Mohammad Mahmoodian and Saeed Rahati, "Sensitivity Analysis of River Flood Routing Model to Input Data," International Journal of Environmental Science and Development vol. 1, no. 3, pp. 256-259, 2010.

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