Machine learning-based pedotransfer functions to predict soil water characteristics curves

Abstract

Soil water characteristic curve (SWCC) is a key property in characterizing unsaturated soil behaviors. Despite considerable progress in predicting methods, predicting SWCCs remains challenging owing to their huge uncertainty. This study exploited the advantages of seven machine learning (ML) models and the unsaturated soil database (UNSODA) to develop a new pedotransfer function (PTF) for estimating SWCC. The importance of UNSODA attributes, including pressure head, soil textural information, state parameters, and particle density, was evaluated using permutation importance and Shapley values. In addition, the performance of ML-PTFs for seven feature selection scenarios was measured based on the evaluated rank of feature importance using Shapley values. The PTF implemented on the extreme gradient boosting (XGB) model yielded the best performance with the highest coefficient of determination of 0.972, which is comparable to the performance documented in the literature. In addition, the pressure head was evaluated as the most important feature, followed by sand fraction, clay fraction, and bulk density. Noticeably, the performance of the seven ML-PTFs converged when the number of features was greater than four (the four most important features), indicating the possibility of excluding silt fraction, particle density, and porosity in developing ML-PTF to predict SWCCs. Finally, to manifest the practical applications the developed XGB-PTF was integrated into the Bayesian optimization to approximate the matric suction profile in Ho Chi Minh City.