Using of Artificial Neural Networks for Evaluation Soil Water Content with Time Domain Reflectometry

Davood Namdar-Khojasteh, Mahdi Shorafa, Mahmoud Omid

Abstract


Time Domain Reflectometry (TDR) has become an established method for soil volumetric water content ( ) measurement. TDR exploits the difference in dielectric constant values between the solid phase, air phase and liquid phase. In this paper, we study and evaluate the ability of empirical models to fit TDR calibration data for the soils of different textures, and adopt artificial neural network (ANN) to predict the Ka–  relationship using soil physical parameters for ten different heavy texture soil types. The explanatory parameters that gave the most significant reduction in the root mean square error (RMSE) were dielectric constant, bulk density, clay content, silt content, sand content and organic matter content. The Ka–  relationship for each soil type was predicted using the other nine soils for calibration purposes. To find the optimum model, various multilayer perceptron (MLP) topologies, having one hidden layer of neurons were investigated.  In this analysis, Ka, bulk density and clay content were selected as input to ANN. The (3-10-1)-MLP, namely a network having 10 neurons in its hidden layer resulted in the best-suited model estimating the soil water content of the heavy texture soils at all soil types. For this topology, R2 and RMSE values were 0.998 and 0.00433, respectively. A comparative study among ANN models and various empirical models was also carried out. ANN models with RMSE and R2 of 0.0043-0.0134 (m3 m-3) and 0.923-0.998, respectively, gave better predictions than empirical models. The ANN model performed superior than both empirical and physical models. Since (3-10-1)-MLP outperformed regression models and it uses only one set of weights and biases for all soil types, it should be preferred over empirical and physical models.


Full Text: PDF DOI: 10.5539/mas.v4n10p76

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This work is licensed under a Creative Commons Attribution 3.0 License.

Modern Applied Science   ISSN 1913-1844 (Print)   ISSN 1913-1852 (Online)

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