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DIVISION S-1—SOIL PHYSICS

Predicting the Dielectric Constant–Water Content Relationship Using Artificial Neural Networks

^a Department of Water Resources Engineering, Lund University, Box 118, SE-221 00 Lund, Sweden
^b Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA
^c Department of Crop Physiology and Soil Science P.O. Box 50 DK-8830 Tjele, Denmark

* Corresponding author (magnus.persson{at}tvrl.lth.se)

Accurate measurements of soil water content () are important in various applications in hydrology and soil science. The time domain reflectometry (TDR) technique has been widely used for measurements during the last two decades. The TDR utilizes the apparent dielectric constant (K_a) for estimations of . The K_a– relationship has been described using both empirical and physical models. Universal calibration equations that fit a wide range of different soil types have been developed. However, to achieve high accuracy, a soil-specific calibration needs to be conducted. In the present study, we use an artificial neural network (ANN) to predict the K_a– relationship using soil physical parameters for ten different soil types. The parameters that give the most significant reduction in the root mean square error (RMSE) are bulk density, clay content, and organic matter content. The K_a– relationship for each soil type is predicted using the other nine for calibration. It is shown that ANN predictions are as good as a soil specific calibration with comparable coefficient of determination and RMSE. Thus, by using ANN, highly accurate data can be obtained without need for elaborate soil specific calibration experiments.

Abbreviations: ANN, artificial neural network • K_a, dielectric constant • RMSE, root mean square error • TDR, time domain reflectometry • , water content

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