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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.04256 |
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| _version_ | 1866916780330450944 |
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| author | Kalimeris, Konstantinos Mindrinos, Leonidas Pallikarakis, Nikolaos |
| author_facet | Kalimeris, Konstantinos Mindrinos, Leonidas Pallikarakis, Nikolaos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04256 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimating properties of a homogeneous bounded soil using machine learning models Kalimeris, Konstantinos Mindrinos, Leonidas Pallikarakis, Nikolaos Geophysics Machine Learning 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. |
| title | Estimating properties of a homogeneous bounded soil using machine learning models |
| topic | Geophysics Machine Learning |
| url | https://arxiv.org/abs/2506.04256 |