2023 Volume 4 Issue 3 Pages 109-118
This research aims to predict the 2D distribution of natural water content in subsurface peat ground using limited survey data. Based on survey results conducted in the Teshio and Ebetsu regions of Hokkaido, Japan, Bayesian estimation of the water content distribution was performed using Gaussian process regression through the application of the Markov chain Monte Carlo (MCMC) method. The accuracy of the predictions was validated using cross-validation techniques. Based on the estimated results, the values of spatial correlation parameters were obtained and revealed that the range of the spatial correlation range in peat ground is shorter compared to typical inorganic soil layer. Furthermore, based on the cross-validation results, the impact of the distance among observation points and prediction locations on the accuracy of the predicted values is discussed. The fitting results of the survey data from the two sites demonstrate the feasibility of estimating the 2D water content distribution in peat ground using Gaussian process regression.
