In Environmental monitoring and assessment
The soil water characteristic curve (SWCC) is of great significance for studying the hydrological cycle, agricultural water management, and unsaturated soil mechanics. However, it is difficult to effectively obtain a large number of SWCCs because of the cumbersome and expensive determination experiments for SWCCs. Pedo-transfer functions (PTFs) established using soil physicochemical properties have become an effective method for solving this problem. However, due to the limitations of the establishment methods and the wide spatial variability of soil properties, it is still difficult to establish PTFs in a specific region. In order to establish the PTFs of SWCCs for the alluvial plain area of the lower reaches of the Yellow River, 233 soil samples were collected from the vadose zone in a typical area. These data were used as the data sources, and eight variables including clay, silt content, fractal dimension, bulk density, total porosity, pH value, organic matter content, and electrical conductivity were used as the influencing factors. By applying and comparing three machine learning algorithms, the PTFs of the SWCCs based on the random forest algorithm were obtained. Based on the Gini index of the random forest, the insensitive factors were eliminated and the optimal variable input mode was constructed. Based on the verification, there was little difference between the predicted water content and the measured water content. The determination coefficient R2 is 0.9308; the root mean square error (RMSE) is 0.0447; and the mean relative error (MRE) is 22.40%.
Zhan Jiang, Li Zhiping, Yu Xiaopeng, Zhao Guizhang, Yuan Qiaoling
Lower reaches of the Yellow River, Machine learning, Pedo-transfer function, Random forest, Soil water characteristic curve