Automatic calibration of SWMM model with adaptive genetic algorithm

Xi Jin, Wenyan Wu, Yinghe Jiang, Jianhua Jin
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引用次数: 6

Abstract

Storm Water Management Model (SWMM) is a popular simulation and management tool for sewer system or storm water management. Since it is a physically based model, the calibration process is necessary before a successful implementation. By separating the calibrated parameters into universal and special styles, the shortcoming of ignore differences among subcatchments' width is conquered, and solution space is also reduced greatly than the way of regarding all calibrated parameters as special parameters. Using flow rate of pipes as objective values, an objective function of difference between simulated results and objective values is build as the objective function of calibration optimal model. A case sewer network is used to evaluate the proposed calibration method, and by comparison with the calibrated results of calibration optimal model using the all universal calibrated parameter selection concept, the advantages of proposed method were summarized.
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基于自适应遗传算法的SWMM模型自动标定
雨水管理模型(SWMM)是一种流行的模拟和管理工具,用于下水道系统或雨水管理。由于它是一个基于物理的模型,在成功实施之前,校准过程是必要的。通过将标定参数分为通用样式和特殊样式,克服了忽略子流域宽度差异的缺点,也比将所有标定参数都作为特殊参数的方法大大减小了求解空间。以管道流量为目标值,建立了模拟结果与目标值之差的目标函数作为标定优化模型的目标函数。以一个实例管网为例,对所提出的标定方法进行了评价,并与采用全通用标定参数选择概念的标定最优模型的标定结果进行了比较,总结了所提出方法的优点。
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