扩域搜索遗传算法优化马斯京根参数及其应用  

Muskingum Parameter Optimization Through Extension Field Search Genetic Algorithm and Its Application

作　　者：李鸿雁[1] 赵娟[1] 王玉新[1] 韩振[1] 王傲[1] 

机构地区：[1]吉林大学地下水资源与环境教育部重点实验室,长春130021

出　　处：《吉林大学学报（地球科学版）》2011年第3期861-865,共5页Journal of Jilin University：Earth Science Edition

基　　金：国家自然科学基金项目(50879028);南京水利科学研究院水文水资源与水利工程科学国家重点实验室开放基金项目(2009491311);水沙科学与水利水电工程国家重点实验室开放研究基金项目(sklhse-2010-A-02)

摘　　要：马斯京根法作为河道洪水预报的重要方法,参数和系数的率定是关键和难点,直接影响其预报精度。在详细阐述扩域搜索遗传算法基本思想和性能分析的基础上,以模拟结果与实测值的误差最小作为进化目标,直接搜索马斯京根法预报方程系数,获得河道上下游流量关系方程。对黄河下游夹河滩至高村的洪水过程进行研究,传统方法的平均绝对误差为240 m3/s,平均相对误差为0.13;遗传算法的平均绝对误差为95 m3/s,平均相对误差为0.05。结果表明:遗传算法精度明显高于传统方法。在实际应用中,对于河道洪水波的传播规律性变化较大的河道,应根据不同量级洪水来模拟洪水的传播规律,并对相应量级的洪水进行预报。The Muskingum method is an important method for the flood prediction.The calibration of parameter and coefficient is a key and difficult point affecting the forecast accuracy.In this paper,we used the minimum error of modelled result matching the measured values as an evolutionary objective on the basis of introducing the basic idea and performance analysis of extension field search genetic algorithm in order to direct search parameter of the Muskingum prognostic equation,so that we could obtain relation equation of upstream and downstream discharge.This paper studied the flood process from Jiahetan to Gaocun in the lower reaches of the Yellow River,and the results showed the mean absolute error of traditional method was 240 m3/s and the mean relative error was 0.13;the mean absolute error of genetic algorithm method was 95 m3/s and the mean relative error was 0.05.It can be seen from the results that the precision of genetic algorithm is significantly higher than that of traditional method.In practice,for the riverways whose flood wave propagating changes a lot,we should simulate the flood propagating according to different flood magnitudes,and then forecast the flood of corresponding magnitudes.

关 键 词：遗传算法 马斯京根法 参数优化 洪水预报 

分 类 号：P641[天文地球—地质矿产勘探]