Using neural networks for calibration of time-domain reflectometry measurements |
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| Authors: | MAGNUS PERSSON RONNY BERNDTSSON BELLIE SIVAKUMAR |
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| Institution: | 1. Department of Water Resources Engineering , Lund University , PO Box 118, S-221 00, Lund, Sweden E-mail: magnus.persson@tvrl.lth.se;2. Department of Land, Air &3. Water Resources , University of California , Davis, California, 95616, USA |
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| Abstract: | Abstract Time-domain reflectometry (TDR) is an electromagnetic technique for measurements of water and solute transport in soils. The relationship between the TDR-measured dielectric constant (Ka ) and bulk soil electrical conductivity (sgrave]a) to water content (θW) and solute concentration is difficult to describe physically due to the complex dielectric response of wet soil. This has led to the development of mostly empirical calibration models. In the present study, artificial neural networks (ANNs) are utilized for calculations of θw and soil solution electrical conductivity (sgrave]w) from TDR-measured Ka and sgrave]a in sand. The ANN model performance is compared to other existing models. The results show that the ANN performs consistently better than all other models, suggesting the suitability of ANNs for accurate TDR calibrations. |
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| Keywords: | neural networks time-domain reflectometry soil water content electrical conductivity |
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