Sacramento模型的多步骤参数估计方法及应用  

Application of Multi-step Parameter Calibration Method with Sacramento Model

作　　者：张刚[1] 解建仓[1] 罗军刚[1] 陈田庆[1] 李建勋[2] 

机构地区：[1]西安理工大学西北水资源与环境生态教育部重点实验室,西安710048 [2]西安理工大学经济与管理学院,西安710048

出　　处：《沈阳农业大学学报》2010年第6期711-716,共6页Journal of Shenyang Agricultural University

基　　金：国家"863"计划项目(2006AA01A126);国家自然科学基金项目(50979088);陕西省国际合作重点项目(2008kw-32)

摘　　要：为了解决传统优化算法在Sacramento模型参数估计中存在早熟、收敛速度慢、容易陷入局部最优和传统求解过程出现模型模拟吻合度较差等问题。对于人工生成的理想水文资料,分别采用SCE-UA算法、并行遗传算法(PGA)、改进粒子群算法(SMSE-PSO)和提出的免疫克隆选择算法(ICSA)进行参数率定,比较结果选出最优算法,同时,将最优算法与多步骤参数估计方法结合进行实测资料的洪水预报,并比较单步骤与多步骤方法的预报效果。结果表明:ICSA收敛结果更好,效率和精度更高,将其与多步骤参数估计结合提高了洪水预报精度。ICSA算法和多步骤参数估计方法结合为Sacramento模型参数估计提供了一条新途径。The study was done in order to overcome the disadvantages of traditional optimal algorithm for estimating parameter of Sacramento： early maturity,poor convergence,local optima and poor simulation precision.For ideal data,SCE-UA,PGA,SMSE-PSO and ICSA were applied to estimate parameters and get comparison result,in the meaning time,combining ICSA and multi-step method was used to forecast flood with measured data and get comparison result of multi-step and single-step method.The comparison results showed that ICSA had higher efficiency and precision in parameter estimation and multi-step greatly improved the forecast precision.Combining ICSA and multi-step will be a new method for Sacramento model＇s parameter estimation.

关 键 词：参数估计 Sacramento模型 免疫 克隆 算法 多步骤 

分 类 号：P332.4[天文地球—水文科学]