群智能优化算法在水文频率曲线适线中的应用  

Application of Swarm Intelligence Optimization Algorithm in Optimization Fitting of Hydrologic Frequency Curve

作　　者：董闯[1,2] 宋松柏[1] 

机构地区：[1]西北农林科技大学,陕西杨凌712100 [2]南京水利科学研究院,江苏南京210029

出　　处：《水文》2011年第2期20-26,共7页Journal of China Hydrology

基　　金：国家自然科学基金项目(50879070;50579065);西北农林科技大学青年学术骨干支持计划和优秀博士论文基金(Z2005)

摘　　要：根据不同适线准则,研究群智能优化算法在水文频率曲线适线中的计算问题,为水利工程规划、水资源优化配置等提供依据。以陕北地区12个主要测站的年径流系列为例,选取五大分布类共12种分布线型,根据我国现行水利水电工程设计洪水计算规范,按照离(残)差平方和最小准则(OLS)、离(残)差绝对值和最小准则(ABS)、相对离差平方和最小准则(WLS),以MATLAB7.6为计算平台,研究模拟退火算法、遗传算法、粒子群算法和蚁群算法进行水文频率参数的估计。在不同适线准则下,陕北地区年径流最优频率分布模型为广义Logistic分布(Generalized Logistic Distribution,GLO),粒子群算法进行参数估算偏差最小。与传统优化方法相比,群智能优化算法对优化目标函数要求低,是一种推求年径流频率曲线统计参数的新途径。According to different curve fitting criterions, the calculation of swarm intelligence optimization algorithm to curve fitting in hydrologic frequency was studied, which can provide the basis for the hydraulic engineering planning and the water resources distributions. Taking the annual runoff data of 12 stations in north region of Shaanxi as an example, choosing 5 distribution types including 12 distributions,according to the regulations of design flood frequency computation for water resources and hydropower projects,applying OLS,ABS and WLS,taking MATLAB7.6 as the computing platform,applying the calculation of Simulated Annealing Algorithm,Genetic Algorithm,Particle Swarm Optimization and Ant Colony Algorithm to parameter estimation in hydrologic frequency. For different curve fitting criterions,all the best frequency distribution models of the annual runoff data in north region of Shaanxi are Generalized Logistic Distribution （GLO）,the method of estimating parameters should select PSO. Comparing with other conventional optimization methods,swarm intelligence optimization algorithm does not dependent on target functions,it is a new way of inferring statistical parameters of annual runoff data frequency curve.

关 键 词：水文频率分析 频率分布模型 参数估计 群智能优化算法 陕北地区 

分 类 号：P333[天文地球—水文科学]