Prediction of soil hydraulic properties by Gaussian process regression algorithm in arid and semiarid zones in Iran

Abstract

The soil water retention curve (SWRC) is one of the principal soil hydraulic properties that is needed as input data in modeling water and solute transport through unsaturated soils. Field or laboratory measurement of SWRC is labor-intensive, expensive and time-consuming. Pedotransfer functions (PTFs) have been developed as an indirect method to predict soil hydraulic properties (e.g. SWRC) from more easily measured soil data by data mining tools. The novelty of the present study is the application of Gaussian process regression (GPR) algorithm as a data mining technique, to predict the SWRC, and comparing its performance with that of the multiple linear regression (MLR) and Rosetta methods, which has not been conducted so far. In this study 15 GPR and MLR-based PTFs were developed to predict the parameters of the van Genuchten model from different combinations of readily available properties of 223 soil samples that were taken from six provinces of Iran. The k-fold (k = 20) cross validation approach was utilized to obtain training and testing data sets for each PTF. The predictions of the GPR and MLR-based PTFs were evaluated by different criteria. The GPR-based PTFs had greater accuracy and reliability than MLR method in predicting SWRC according to integral root mean square error (IRMSE) criterion. However, the differences were not significant (P > 0.05) in the testing step, but the reliability of both methods were significantly (P < 0.05) better than Rosetta-based PTFs. The covariance functions of GPR method can be effectively fitted by kernels with different features for modeling complex relationships. The GPR method can be considered as a competitive alternative to develop parametric-PTFs.