自适应随机搜索算法在水文模型参数优选中的并行效率研究

Research on parallel efficiency of adaptive random search method in parameter optimization of hydrological model

水文模型参数的快速率定是山洪以及中小河流洪水预报预警中的重要研究内容之一。参数自动优选功能可以极大地提高水文模型的使用效率,随机搜索算法是大多数参数全局优选算法基础,但却因为耗时较长而应用较少。以Brooks的自适应随机搜索算法(adaptive random search method)为对象、利用.NET的Parallel对象进行了CPU并行改造,利用英伟达的CUDA对象进行了GPU+CPU并行改造,并以缅甸境内其培河子流域上的新安江模型为优选对象,比较了优选效果和计算效率。研究表明:ARS算法和SCE-UA的优选结果相当,并行改造后的ARS算法计算效率有显著提高。研究成果对水文模型应用时参数优选算法的比选具有重要的参考价值。

The rapid calibration of hydrological model parameters is one of the important research contents in flood forecasting and early warning of flash floods and riverine floods in middle and small-sized rivers. The automatic parameter optimization function can greatly improve the efficiency of hydrological models. The random search algorithm is the basis of most global parameter optimization algorithms, but it is seldom used because of time-consuming. Brooks’ adaptive random search method as the object and.Net parallel object are used for CPU parallel transformation, and NVIDIA CUDA object is used for GPU+CPU parallel transformation. The Xin’anjiang Model on the Qipei River sub watershed in Myanmar is taken as the optimization object, and the optimization effect and calculation efficiency are compared. The research results show that the optimization results of ARS algorithm and SCE-UA are equivalent, and the computing efficiency of ARS algorithm after parallel transformation is significantly improved. The research results have important reference value for the comparison and selection of parameter optimization algorithms in the application of hydrological models.