| Summary: | 碩士 === 中原大學 === 土木工程研究所 === 98 === This research integrates Storm Water Management Model(SWMM) and Genetic Algorithm Library(GAlib), into a model that optimizes SWMM-RUNOFF parameters automatically(SWMM-RUNOFF Parameters Optimization Model, named SRPOM), the SRPOM model calibrate the hydrological and geographical parameters of SWMM-RUNOFF automatically to reduce the cost of time in calibrating parameters, and it will promote the efficiency of calibrating parameters in order to achieve the hydrological and geographical parameters of SWMM-RUNOFF optimization automatically. The 4th drainage system, Zhung-Gung drainage system, in Taipei city is employed as a case study. Furthermore, we selected nine of the typhoon events during 2004 to 2006 which were considered to be the training, verification, and examination data for SRPOM model.
Seven typhoon events were considered to be the training data for SRPOM model in order to compute the optimal parameters; two typhoon events(typhoon Saomai, typhoon Bopha) were considered to be the verification data for the influence on the optimal parameters and the default parameters with the manhole water level of Zhung-Gung_1 where in the Zhung-Gung drainage system, the simulation indicated that manhole water level hydrograph in the optimal parameter sets were much more conformed to the actual manhole water level hydrograph. In the examination data(typhoon Bopha), if we were not considered the percentage error that objective function will got the lower convergence than restrained the percentage error; Furthermore, when we were in the optimizes parameters process for SRPOM model, depended on different percentage error, we could set up different GA operator to promote the convergence efficiency of objective function.
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