Document Type : Original Article

Authors

• Samira mesry ¹
• Hossein Shirani ²
• abolghasem Kamkarrohani ³
• Hamidreza Motaghian ⁴

¹ Shahrekord university

² Professor of Soil Science, Department of Soil Science, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

³ shahrood university

⁴ soil science department of Shahrekord University

Abstract

Nowadays, the use of new methods to estimate hydraulic parameters such as soil moisture characteristic curve is considered. The aim of this study was to determine the effective factors in modeling of moisture characteristic curve parameters, from conveniently available, by the decision tree and error estimator cross validation and resub stitution were used. In this study, 72 soil samples were collected from six different tissues from the village of Margalomk and Shahrekord city. Conveniently available soil properties were introduced into software in 2 scenarios (the first scenario %sand, %clay, OM%, CaCO₃, BD, pH, EC, mean weight diameter of dry aggregate (MWD _dry), mean weight diameter of wet aggregate (MWD _wet) and θ_s, the second scenario %sand, %clay, OM%, CaCO₃, BD, %gravel, electrical resistivity, dielectric constant, root penetration resistivity). The results showed that the correlation coefficient for the PWP target variables in the first scenario is 0.88 and in the second scenario the maximum value for the FC target variable is 0.93. By replacing, geophysical and mechanical properties in the second scenario, the correlation coefficient for the variables FC and α Which are affected by the structure and texture of the soil increased, and decreased for PWP, n and m variables, which are more affected by soil texture. %RMSE was also slightly lower for the FC and α variables in the second scenario than in the first scenario, but in general according to% RMSE, modeling for all variables was successful in both scenarios.

Keywords

• Dielectric constant
• Electrical resistivity
• Mechanical resistivity
• Artificial intelligence