董艳辉, 李国敏, 郭永海, 徐海珍. 2010: 应用并行PEST算法优化地下水模型参数. 工程地质学报, 18(1): 140.
    引用本文: 董艳辉, 李国敏, 郭永海, 徐海珍. 2010: 应用并行PEST算法优化地下水模型参数. 工程地质学报, 18(1): 140.
    DONG Yanhui, LI Guomin, GUO Yonghai, XU Haizhen. 2010: OPTIMIZATION OF MODEL PARAMETERS FOR GROUNDWATER FLOW USING PARALLELIZED PEST METHOD. JOURNAL OF ENGINEERING GEOLOGY, 18(1): 140.
    Citation: DONG Yanhui, LI Guomin, GUO Yonghai, XU Haizhen. 2010: OPTIMIZATION OF MODEL PARAMETERS FOR GROUNDWATER FLOW USING PARALLELIZED PEST METHOD. JOURNAL OF ENGINEERING GEOLOGY, 18(1): 140.

    应用并行PEST算法优化地下水模型参数

    OPTIMIZATION OF MODEL PARAMETERS FOR GROUNDWATER FLOW USING PARALLELIZED PEST METHOD

    • 摘要: 基于列文伯格-马夸尔特(Levenberg-Marquardt)算法的PEST参数优化程序具有寻优速度快、健壮性好的优点,在地下水模型参数优化研究中有许多成功的应用实例。但是,对于大尺度、高精度和高复杂性的大规模地下水模拟,使用PEST进行参数优化需要大量的计算时间,优化效率较低。本文应用OpenMP并行编程方法对PEST算法进行了并行化,使之可以在共享存储并行计算机上进行参数优化的并行计算。并将此方法应用于甘肃北山区域地下水模型的参数优化中,并行实验表明,使用并行化的PEST可以将地下水模型参数优化效率提高3.7倍。 

       

      Abstract: During the last decades, a number of methods have been developed for the optimization of hydrologic model parameters. One frequently used method is the PEST method which is a LevenbergMarquardtbased optimization algorithm. As a nonlinear parameter estimator, the PEST method can exist independently of any particular models, can estimate parameters and/or excitations, and can carry out various predictive analysis tasks. Its model types cover a wide range. It is preferable to calibrate a MODFLOW model using the PEST method in many situations. However, for a largescale, highresolution or highcomplexity groundwater flow modeling, the parameter optimization requires massive computing time. In this study, the OpenMP programming paradigm is used to achieve the modest parallelism on a sharedmemory computer for the PEST method. Application of the parallel PEST method to Beishan area, Gansu province shows that the computing speed can be increased up to 3.7 times. The computing speed is defined as the ratio of the serial computing time and the parallel computing time. The parallel PEST parameter optimization method can be an useful and effective tool for large models.

       

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