PARAMETER IDENTIFICATION FOR MODELING RIVER NETWORK USING A GENETIC ALGORITHM
PARAMETER IDENTIFICATION FOR MODELING RIVER NETWORK USING A GENETIC ALGORITHM
复制标题
使用遗传算法进行河网建模的参数识别
DOI：
10.1016/s1001-6058(09)60051-2
复制
发表时间：
2010-04-01
影响因子：
2.5
通讯作者：
Xiao Yang
中科院分区：
文献类型：
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作者：
Tang Hong-wu;Xin Xiao-kang;Xiao Yang
一维河网的模拟需要求解圣维南方程组，而圣维南方程组中的参数对模型的精度有很大的影响。试凑法是一种常用的参数标定方法，但这种方法耗时长，需要经验来选择合适的参数值。因此。本文将水动力学模型与遗传算法（GA）技术的智能模型相结合，提出了一种能自动优化参数的智能仿真方法。它对实测数据的依赖性较低，模型精度较高。将优化后的参数引入到水动力数值模型中，模拟结果与现场实测数据吻合较好
The simulation of a one-dimensional river network needs to solve the Saint-Venant equations, in which the variable parameters normally have a significant influence on the model accuracy A Trial-and-Error approach is a most commonly adopted method of parameter calibration, however, this method is time-consuming and requires experience to select the appropriate values of parameter. Consequently. simulated results obtained via this method usually differ between practitioners This article combines a hydrodynamic model with an intelligent model originated from the Genetic Algorithm (GA) technique, in order to provide an intelligent simulation method that can optimize the parameters automatically Compared with current approaches, the method presented in this article is simpler. its dependence on field data is lower, and the model accuracy is higher. When the optimized parameters are taken into the hydrodynamic numerical model, a good agreement is attained between the simulated results and the field data