ABSTRACT

Seventy-two infiltration tests were conducted to model soil water infiltration variability using scaling in the first and third terraces of Yangling District, respectively. The relationship between the scaling factor of the Kostiakov–Lewis equation and soil properties for multiple scales was determined through two-dimensional continuous wavelet transform (2D-CWT) and path analyses, and the pedotransfer functions (PTFs) based on the support vector machine (SVM) and back propagation (BP) neural network were developed for estimating the scaling factor. The results indicated that when scaling factor was calculated using the least square method (F_S), the best match was achieved between the predicted infiltration and measured values. In the first and third terraces, the results of 2D-CWT and path analyses indicated that F_S were positively correlated with sand content, silt content, and soil organic matter content. However, F_S was negatively correlated with bulk density in the first terrace, and which was negatively correlated with clay content and initial water content in the third terrace. The accuracy of SVM-based PTF is higher than BP-based PTF, the mean absolute value of relative errors of SVM-based PTF were 11.3% and 8.16% for the first and third terrace, respectively. Therefore, the SVM is preferred for PTF development.

摘要

分别在杨凌区第一梯田和第三梯田进行了 72 次入渗试验，使用缩放比例模拟土壤水分入渗变异性。通过二维连续小波变换（2D-CWT）和路径分析，以及基于支持向量机（ SVM）和反向传播（BP）神经网络被开发用于估计比例因子。结果表明，当使用最小二乘法计算比例因子时（F _S), 预测的渗透和测量值之间实现了最佳匹配。2D-CWT和路径分析结果表明，在一级和三级阶地，F _S与含砂量、含泥量和土壤有机质含量呈正相关。而F _S在第一阶地与容重呈负相关，在第三阶地与粘土含量和初始含水量呈负相关。基于 SVM 的 PTF 的精度高于基于 BP 的 PTF，基于 SVM 的 PTF 的相对误差的平均绝对值对于第一和第三阶梯分别为 11.3% 和 8.16%。因此，SVM 是 PTF 开发的首选。