Application of Artificial Neural Networks in Modeling Soil Solution Electrical Conductivity

作者：Namdar Khojasteh Davood^*; Shorafa Mahdi; Omid Mahmoud; Fazeli Shaghani Mahmoud

来源：Soil Science, 2010, 175(9): 432-437.

DOI：10.1097/SS.0b013e3181f2a2e9

• Electrical conductivity
• linear model
• neural networks
• time domain reflectometry

In various applications in soil science and agriculture, there is a need for accurate measurements of soil solution electrical conductivity (EC). Time-domain reflectometry (TDR) has become an important means for measurement of soil water content and bulk EC. The TDR-measured EC (sigma(a)) and dielectric constant (K(a)) can be used to calculate the soil solution EC (sigma(p)). Hilhorst (2000) found that using this linear relationship, measurements of sigma(p) can be made in a wide range of soil types without soil-specific calibrations. In the present study, the linear model was evaluated using detailed TDR. We also attempted to model the sigma(p)-sigma(a)-K(a) relationship using artificial neural networks (ANN). To develop ANN models, we used TDR to measure K(a) and sigma(a) along with five soil physical parameters (sand, silt, clay, organic matter content, and bulk density) in 10 different soil types. In total, 265 K(a) and sigma(a) measurements were obtained. The ANN estimation of sigma(p) was found to have mean square error values between 0.071 and 0.41 dS m(-1) for the 10 different soil types, whereas the mean square error of the linear model was 0.315 dS m(-1). A sensitivity analysis showed that the ANN model was more sensitive to sigma(a) and two soil physical parameters (organic matter and clay content) more than other inputs as they affected the sigma(p)-sigma(a)-K(a) relationship.

• 出版日期2010-9