Performance Investigation of Nakagami- m Distribution to Derive Flood Hydrograph by Genetic Algorithm Optimization Approach

In this study, two-parameter Nakagami- m distribution has been introduced along with two-parameter gamma (GM), three-parameter generalized logistic (GLG), three-parameter Pearson type 3 (PT3), and three-parameter generalized extreme value distributions for the derivation of flood hydrographs for gau...

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Bibliographic Details
Published in:Journal of hydrologic engineering 2010-08, Vol.15 (8), p.658-666
Main Authors: Sarkar, S, Goel, N. K, Mathur, B. S
Format: Article
Language:English
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Summary:In this study, two-parameter Nakagami- m distribution has been introduced along with two-parameter gamma (GM), three-parameter generalized logistic (GLG), three-parameter Pearson type 3 (PT3), and three-parameter generalized extreme value distributions for the derivation of flood hydrographs for gauged catchments. Parameters of these distributions have been optimized using genetic algorithm (GA) in MATLAB v. 7.1 GA toolbox. Observed storm events of three catchments have been used to evaluate the efficiency of the hydrographs generated by these distributions. A validation test has been performed on the different storm event data set of one of the catchments and results were found satisfactory. Statistical estimators (viz., coefficient of efficiency, root-mean squared error, and mean absolute percent error), which indicate the efficiency of the optimization technique in deriving flood hydrographs have been used to find out the rank of these distributions for deriving the hydrographs for the particular catchments.
ISSN:1084-0699
1943-5584
DOI:10.1061/(ASCE)HE.1943-5584.0000220