基于SGA和SRFR的畦灌入渗参数与糙率系数优化反演模型(Ⅰ)——模型建立

An optimized inverse model used to estimate Kostiakov infiltration parameters and Manning's roughness coefficient based on SGA and SRFR model:(Ⅰ) Establishment

利用基于地表非稳定流运动方程的地面灌溉模型对畦灌性能进行分析评价是改进传统地面灌溉方式、提高畦灌性能的重要举措,而准确地确定畦灌土壤入渗参数和田面糙率系数是改善畦灌性能模拟精度的重要前提。本文基于基本遗传算法SGA和地面灌溉一维模型SRFR建立的畦灌土壤入渗参数与田面糙率系数优化反演模型,可在给定的参数值域范围内开展多参数全局性寻优,利用计算机动态解析技术对SRFR模型执行程序进行解析处理,建立SGA与SRFR模型间的内部运行衔接方式,实现畦灌土壤入渗参数与田间面糙率系数优化反演过程的自动化运作,进而有效提高了参数寻优估值的工作效率。

The surface irrigation simulation model has been widely used for the evaluation of performances of surface irrigation system. Determination of parameters and coefficient involved in simulating surface irrigation become important precondition to achieve precision simulation. In the paper a optimized inverse model used to estimate Kostiakov infiltration parameters and Manning＇s roughness coefficient under basin irrigation condition, based on Simple Genetic Algorithm（SGA） and Surface Irrigation Simulation Model SRFR, is developed to globally optimize infiltration parameters and roughness coefficient in given range.The computer dynamic analysis technique is applied for analyzing of the model SRFR and the innerrunning joint mode between the SGA and SRFR is set up. Therefore, the optimization of Kostiakov infiltration parameters and Manning＇ s roughness coefficient by the inverse model can be run automatically, consequently improving the working efficiency of the parameters optimization.