Volume 24, Issue 3 (Fall 2020)                   jwss 2020, 24(3): 269-289 | Back to browse issues page

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Chavoshi Borujeni S, Shirani K. Evaluating the Performance of the Artificial Bee Colony Algorithm in Flood Frequency Analysis. jwss 2020; 24 (3) :269-289
URL: http://jstnar.iut.ac.ir/article-1-3940-en.html
1. Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran. , chsatar@gmail.com
Abstract:   (2385 Views)
Selection of the appropriate distribution function and estimation of its parameters are two fundamental steps in the accurate estimation of flood magnitude. This study relied on the concept of optimization by meta heuristic algorithms to improve the results obtained from the conventional methods of parameter estimation, such as maximum likelihood (ML), moments (MOM) and probability weighted moments (PWM) methods. More specifically, this study aimed to improve flood frequency analysis using the Artificial Bee Colony algorithm (ABC). The overall performance of this algorithm was compared to the conventional methods by employing goodness of fit statistics, correlation coefficient (CC), coefficient of efficiency (CE) and root mean square error (RMSE). The study area, Babolrood catchment located in southern bank of Caspian Sea, has been subjected to annual flooding events. A total of 6 hydrometry stations in the study area were delineated and their data were used in the analysis of 6 distribution functions of Normal, Gumbel, Gamma, Pearson Type 3, General Extreme Value and General Logistic. This analysis indicated that Gamma and Pearson Type 3 were the most appropriate distribution functions for flood appraisal in the study area, according to the ABC and conventional methods, respectively. Also, the results showed that ABC outperformed ML, MOM and PWM; so, Gamma could be recommended as the most reliable distribution function for flood frequency analysis in the study area.
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Type of Study: Research | Subject: Ggeneral
Received: 2019/09/29 | Accepted: 2020/05/3 | Published: 2020/11/30

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