Utilization of Gaussian Process Regression for Determination of Soil Electrical Resistivity

Soil electrical resistivity (RE) is an important parameter for geotechnical engineering projects. This article employs Gaussian process regression (GPR) for prediction of RE of soil based on soil thermal resistivity (R-T), percentage sum of the gravel and sand size fractions (F), and degree of saturation (S-r). GPR is derived from the perspective of Bayesian nonparametric regression. Two models (Model I and Model II) have been developed. The developed GPR has been compared with the artificial neural network. It gives the variance of the predicted RE. The results show the developed GPR is an efficient tool for prediction of RE of soil.