Comparison of genetic algorithms and shuffled complex evolution approach for calibrating distributed rainfall–runoff model |
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| Authors: | Yu‐Chi Wang Pao‐Shan Yu Tao‐Chang Yang |
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| Institution: | 1. Taiwan Typhoon and Flood Research Institute, National Applied Research Laboratories, No. 22, Keyuan Road, 40763 Taichung, Taiwan, ROC;2. Department of Hydraulic and Ocean Engineering, National Cheng‐Kung University, No. 1, University Road, 701 Tainan, Taiwan, ROC;3. Department of Information Management, Hsing‐Kuo University of Management, No. 600, Section 3, Taijing Blvd., 709 Tainan, Taiwan, ROC |
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| Abstract: | Three methods, Shuffled Complex Evolution (SCE), Simple Genetic Algorithm (SGA) and Micro‐Genetic Algorithm (µGA), are applied in parameter calibration of a grid‐based distributed rainfall–runoff model (GBDM) and compared by their performances. Ten and four historical storm events in the Yan‐Shui Creek catchment, Taiwan, provide the database for model calibration and verification, respectively. The study reveals that the SCE, SGA and µGA have close calibration results, and none of them are superior with respect to all the performance measures, i.e. the errors of time to peak, peak discharge and the total runoff volume, etc. The performances of the GBDM for the verification events are slightly worse than those in the calibration events, but still quite satisfactory. Among the three methods, the SCE seems to be more robust than the other two approaches because of the smallest influence of different initial random number seeds on calibrated model parameters, and has the best performance of verification with a relatively small number of calibration events. Copyright © 2009 John Wiley & Sons, Ltd. |
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| Keywords: | distributed rainfall– runoff model automatic calibration genetic algorithms shuffled complex evolution approach |
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