Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity
| Autor: | Annika Badorreck, Davood Moghadas |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
Artificial neural network business.industry Reference data (financial markets) Petrophysics Geophysics 010502 geochemistry & geophysics Machine learning computer.software_genre 01 natural sciences Electrical resistivity and conductivity Soil water Capacitance probe Electrical resistivity tomography Artificial intelligence business Water content computer Geology 0105 earth and related environmental sciences |
| Zdroj: | Near Surface Geophysics. 17:181-195 |
| ISSN: | 1873-0604 1569-4445 |
| Popis: | Soil water content (θ) is a key variable in different earth science disciplines since it mediates the water and energy exchange between the surface and atmosphere. Electrical and electromagnetic geophysical techniques have been widely used to estimate soil electrical conductivity (σ) and soil moisture. However, obtaining the σ−θ relationship is not straightforward due to the non‐linearity and also dependency on many different soil and environmental properties. The purpose of this paper is to determine if artificial neural network is an appropriate machine learning technique for relating electrical conductivity to soil water content. In this respect, time‐lapse electrical resistivity tomography measurements were carried out along a transect in the Chicken Creek catchment (Brandenburg, Germany). To ensure proper retrieval of the σ and θ, reference values were measured near the beginning of the transect via an excavated pit using 5TE capacitance sensors installed at different depths. We explored robustness and pertinence of the artificial neural network approach in comparison with Rhoades model (as a commonly used petrophysical relationship) to convert the inversely estimated σ from electrical resistivity tomography to the θ. The proposed approach was successfully validated and benchmarked by comparing the estimated values with the reference data. This study showed the superiority of the artificial neural network approach to the Rhoades model to obtain σ−θ relationship. In particular, artificial neural network allowed for more accurate estimation of the temporal wetting front than the petrophysical model. The proposed methodology thus offers a great promise for deriving spatiotemporal soil moisture patterns from geophysical data and obtaining the in situσ−θ relationship, taking into account the non‐linear variations of the soil moisture. |
| Databáze: | OpenAIRE |
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