This work focuses on estimating soil properties from water moisture measurements. We consider simulated data generated by solving the initial-boundary value problem governing vertical infiltration in a homogeneous, bounded soil profile, with the usage of the Fokas method. To address the parameter identification problem, which is formulated as a two-output regression task, we explore various machine learning models. The performance of each model is assessed under different data conditions: full, noisy, and limited. Overall, the prediction of diffusivity D tends to be more accurate than that of hydraulic conductivity K. Among the models considered, Support Vector Machines (SVMs) and Neural Networks (NNs) demonstrate the highest robustness, achieving near-perfect accuracy and minimal errors.
@article{arxiv.2506.04256,
title = {Estimating properties of a homogeneous bounded soil using machine learning models},
author = {Konstantinos Kalimeris and Leonidas Mindrinos and Nikolaos Pallikarakis},
journal= {arXiv preprint arXiv:2506.04256},
year = {2025}
}