Mojtaba Khoshravesh - Assistant Professor, Water Engineering Department, Sari Agricultural Sciences and Natural Resources University, *Corresponding author’s
Mahsa Pesarakloo - MSc student, Water Engineering Department, Sari Agricultural Sciences and Natural Resources University,
Pooya Shirazi - PhD student of Irrigation and Drainage, College of Agriculture, Water Engineering Department, Ferdowsi University of Mashhad,

Due to the limited water resources in agriculture, using to any ways to save water and also increase the acreage is very important. The objectives of this study were to predict the soil moisture content and soil electrical conductivity using artificial neural network model and to identify the most important independent properties affecting the soil moisture content and soil electrical conductivity under deficit irrigation and irrigation water salinity. Prediction of the artificial neural network models resulted R2=0.82-0.86 and root mean square error of 0.71-1.5 for soil moisture content and R2=0.78-0.85 and root mean square error of 0.08-0.38 for soil electrical conductivity, respectively. Our results indicated that the artificial neural network models could explain 0.78-0.86 % of the total variability in soil moisture content and soil electrical conductivity.

Artificial neural network modeling, soil moisture content, soil electrical conductivity