Estimating properties of a homogeneous bounded soil using machine learning models

• URL: http://arxiv.org/abs/2506.04256v1
• Date: Mon, 02 Jun 2025 07:25:03 GMT
• Title: Estimating properties of a homogeneous bounded soil using machine learning models
• Authors: Konstantinos Kalimeris, Leonidas Mindrinos, Nikolaos Pallikarakis,
• Abstract summary: This work focuses on estimating soil properties from water moisture measurements.<n>We consider simulated data generated by solving the initial-boundary value problem governing vertical infiltration in a homogeneous, bounded soil profile.<n>To address the parameter identification problem, which is formulated as a two-output regression task, we explore various machine learning models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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