摘要
为了优化雨水径流污染模型参数,比较了四种不依赖模型具体数学表达形式的基于最小二乘法原理的参数估计算法,包括拟牛顿法、单纯形法、模式搜索算法和遗传算法,还比较研究了四种优化算法在收敛速度,收敛稳定形和准确性及初始值等方面对优化结果的影响.研究结果表明,拟牛顿法收敛速度最快,单纯形法稳定性最好,遗传算法对初始值最不敏感.在此基础上,提出优化雨水径流污染模型参数的计算方法,应结合遗传算法和单纯形等方法进行,该方法能够利用多次降雨事件记录的径流污染数据,同时优化污染物累积模型和污染物暴雨冲刷模型参数,为雨水径流污染模拟的模型选择和参数优化提供了较好的通用方法.
Four optimization algorithms （Genetic, Pattern search, Quasi-Newton and Simplex）for storm water pollutant model optimization were introduced. Three rainfall runoff data and a kind of buildup and washoff model were selected for this study. The initial value of the genetic algorithmic parameter was analyzed and discussed. The converge speed, the initial parameter value and the different combinations of the four algorithms were comparatively studied in detail. The results show that each algorithm has its own advantage. Genetic is insensitive to the initial parameter value, Quasi-Newton is the fastest algorithm and Simplex has the highest search stability. It is suggested that the combination of Genetic, Quasi-Newton and Simplex algorithms should be used for optimized storm water pollutant model parameters in order to obtain the best parameter values.
基金
国家科技部城市水环境专项课题(2003AA60100-3-4)资助
关键词
雨水径流污染模型
参数优化
遗传算法
单纯形法
拟牛顿法
模式搜索算法
runoff pollutants model
parameter calibration
Genetic algorithm
Pattern search algorithm
Quasi-Newton algorithm
Simplex algorithm
