Predicting the dielectric constant-water content relationship using artificial neural networks

Accurate measurements of soil water content (theta) are important in various applications in hydrology and soil science. The time domain reflectometry (TDR) technique has been widely used for theta measurements during the last two decades. The TDR utilizes the apparent dielectric constant (K-s) for estimations of theta. The K-a-theta 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-theta 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-theta 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.