Enhancing TDR based water content measurements by ANN in sandy soils

Abstract

Time Domain Reflectometry (TDR) has become an established and reliable electromagnetic method for quantification of the soil water content (SWC). The existing calibration models used to convert the dielectric permittivity to SWC cannot be assumed to be universal, mainly due to the fact that the dielectric permittivity of the soils depends upon various soil specific properties. This paper focuses on development of a methodology using Artificial Neural Network (ANN) for enhanced determination of SWC based on dielectric permittivity measurement via TDR. Within this study, the dielectric permittivity obtained with the TDR measurement, the bulk dry density, the specific gravity and the fines content were selected as the input parameters for an ANN model. The performance of the ANN model has been compared with some of the existing calibration models. The average RMSE of the ANN-based approach was 0.009cm3cm−3 and 0.007cm3cm−3 with 8 and 11 nodes, respectively, while the same value was in a range of 0.019 to 0.033cm3cm−3 for the existing calibration models and 0.015 to 0.018cm3cm−3 for the best-fitted calibration models when all soil classes considered together. The results showed that the SWC predicted by the ANN model as proposed in this study is more accurate than that of the existing calibration models for sandy soils.