Kostiakov入渗模型参数的预测精度对比分析研究  

A Comparative Analysis and Study on Forecast Precision of Parameters of Kostiakov Infiltration Models

作　　者：李昊哲[1] 樊贵盛[1] 

机构地区：[1]太原理工大学水利科学与工程学院,太原030024

出　　处：《节水灌溉》2017年第11期27-30,共4页Water Saving Irrigation

基　　金：国家自然科学基金项目"区域尺度上土壤入渗参数多元非线性传输函数研究"(40671081)

摘　　要：基于黄土高原系列大田入渗试验数据,以土壤体积含水率、干容重、黏粒含量、粉粒含量、有机质含量为输入因子,采用支持向量机和BP神经网络两种算法,对Kostiakov二参数入渗模型参数进行预测,并对两种算法下预测结果的相对误差值进行分析,结果表明:采用支持向量机算法对入渗系数和入渗指数进行预测的结果相对误差最大值和平均值都比BP算法的预测结果要小,相对误差最小值比BP算法的预测结果要大;支持向量机算法比BP算法所得预测结果的稳定性好,精确度高。研究结果丰富了采用土壤传输函数获取入渗参数这一研究方向,同时为获取更高精度的入渗参数在方法的选取上提供一定的理论依据。Based on the land infiltration test data of the Loess Plateau, the parameters of the Kostiakov infiltration model were predicted by using two algorithms of support vector machine and BP neural network with the input parameters of the soil water content, bulk density, clay content, silt particle content and organic matter. And the relative errors of the prediction results were analyzed. The results showed that the maximum relative error value and the average relative error value of the prediction results of infiltration coefficient and infiltration index by using support vector machine were both smaller than those by using BP neural network while the least relative error value was bigger. The stability of the prediction results by support vector machine was better than that by BP neural network and the precision was higher. The results enriched the research trend of determining infiltration parameters with pedo-transfer functions and provided a theoretical basis for the selection of the method to obtain more precise infiltration parameter.

关 键 词：支持向量机 BP神经网络 Kostiakov入渗模型 误差分析 土壤理化参数 

分 类 号：S152[农业科学—土壤学] TV93[农业科学—农业基础科学]