Scaling of Kostiakov-Lewis equation and estimation of scaling factors at field scale

• Published in: Archiv für Acker- und Pflanzenbau und Bodenkunde Vol. 69; no. 4; pp. 632 - 647
• Main Authors: Feng, Zhengjiang, Nie, WeiBo
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 21.03.2023; Taylor & Francis Ltd
• Subjects: agronomy; Back propagation networks; back propagation neural network; Bulk density; clay fraction; Continuous wavelet transform; equations; Estimation; Infiltration; infiltration (hydrology); Kostiakov-lewis equation; Mathematical analysis; Moisture content; Neural networks; Organic matter; sand fraction; Scaling; scaling factor; Scaling factors; silt fraction; Soil infiltration; Soil organic matter; Soil properties; Soil water; support vector machine; Support vector machines; Terraces; Two dimensional analysis; Water content; Water infiltration; wavelet; wavelet analysis; Wavelet transforms
• ISSN: 0365-0340; 1476-3567; 1476-3567
• DOI: 10.1080/03650340.2021.2022124

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.