文章基本信息

标题：Comparison of Particle Swarm Optimization and Shuffle Complex Evolution for Auto-Calibration of Hourly Tank Model’s Parameters
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作者：Kuok King Kuok ; Sobri Harun ; Po-Chan Chiu 等
期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2011
卷号：3
期号：3
出版社：International Center for Scientific Research and Studies
摘要：The famous Hydrological Tank Model is always preferred for runoff forecasting. This main reason is Tank Model not only simple in term of its structures, but able to forecast runoff accurately using only rainfall and runoff data. However, much time and effort are required to calibrate a large numbers of parameters in the model for obtaining better results through trial-and-error procedure. Therefore, there is an urgent need to develop an auto-calibration method. Two types of global optimization methods (GOMs), named as Particle Swarm Optimization (PSO) and Shuffle Complex Evolution (SCE) are selected. The selected study area is Bedup basin, Samarahan, Sarawak, Malaysia. Input data used for model calibration are hourly rainfall and runoff only. The accuracy of the simulation results are measured using Coefficient of Correlation (R) and Nash-Sutcliffe Coefficient (E2). The robustness of the model parameters obtained are further analyzed with boxplots analysis. Peak errors are also evaluated to determine the difference between the observed and simulated peaks. Results revealed that the performance of simple PSO method is slightly better than the famous and complicated SCE method. PSO is able to obtain optimal values for 10 parameters fast and accurate within a multidimensional parameter space that could provide the best fit between the observed and simulated runoff
关键词：Hydrological Tank Model; particle swarm optimization (PSO); shuffle complex ;evolution (SCE); rainfall-runoff model