Estimation of Soil Salinity Profile in Tabriz Irrigation and Drainage Network Using SaltMod and ANN Models

آب و خاک

Article 17, Volume 25, Issue 1, March 2011   

Document Type: Research Article

DOI: 10.22067/jsw.v0i0.8518

Authors

A. Haghverdi^{* 1}; K. Mohammadi²; S.A. Mohseni Movahed³; B. Ghahraman¹; M. Afshar⁴

¹Faculty of Agriculture, Ferdowsi University of Mashhad

²Department of Irrigation and Drainage Engineering, Tarbiat Modares University of Tehran

³Department of Water Engineering, Faculty of Agriculture, University of Arak

⁴Yekom Consulting Engineers, Tehran

Abstract

Abstract
Soil salinity within plant root zone is one of the most important problems that cause reduction in yield in agricultural lands. In this research, salinity in soil profile was simulated in Tabriz irrigation and drainage network using SaltMod and Artificial Neural Networks (ANNs) models. Based on initial spatial distribution of salinity in soil profile, studying area was divided to 4 different soil and water groups and for two seasons in one year salinity was predicted. The SaltMod model was calibrated and then was applied to generate 2400 data sets for training ANN models. Some of the input data of SaltMod were used in ANN models including irrigation water depth, evapotranspiration, water table depth, rainfall, and initial soil salinity. Efficiency of genetic algorithm in training phase of ANNs was analyzed. The mean of correlation coefficient (R2) and root mean square error (RMSE) of estimated salinity in all groups was 0.8 and 0.032 respectively. In conclusion ANNs could perform well in simulation of soil salinity and it could be replaced SaltMod with enough accuracy. The results showed that overall performance of ANN models improve by applying genetic algorithm.

Keywords: Tabriz plain, Soil profile salinity, Genetic Algorithm, Artificial Neural Networks, SaltMod