Iman Javadzarin - Master graduated of soil chemistry, department of soil science engineering, faculty of agricultural engineering and technology, college of agriculture and natural resources, university of Tehran, Karaj, Iran
Farokh Tavakolian - Master graduated of irrigation and drainage , department of irrigation and drainage engineering, faculty of agricultural engineering, Ahwaz Azad University Khuzestan, Ahwaz, Iran
Behzad Hojati - Master graduated of construction management, department of civil engineering, faculty of engineering, Ahwaz Azad University, Khuzestan, Ahwaz, Iran
Hamid Reza Ghareh Mohammadlu - PhD graduated of irrigation and drainage, department of irrigation and drainage engineering, faculty of agricultural engineering

Artificial neural network (ANN) and multivariable regression (MVR) models are known as two best and most common models to predication and estimation of environmental parameters. One of the most effective neural network modeling is the multilayer perceptron (MLP) that known as a supervised network. Multivariate regression (MVR) model is usually used to find the relevant coefficients in the model equation. Generalization is a big limitation for MVR models. Soil saturated hydraulic conductivity (Ks) parameter is one of the most widely used soil hydraulic properties for modeling the movement of water in soils and for designing drainage and irrigation water management practices and depend on soil structure, soil texture, chemical properties (EC, pH, CEC, content of ions), organic matter content and bulk density. Measurement of Ks is relatively time-consuming and become infeasible when hydrologic estimates are needed for large areas. Minoo Island is an area in the Khuzestan province, in southwestern Iran and is close to the city of Abadan. There are not enough information about Minoo soils. Aims of this research are including comparison between ANN and MVR models, create, modify and select suitable model to estimate Ks by a few easily measured soil properties and investigation on whether or not application MVR and ANN models in study area to estimate Ks parameter. Sampling operations in order to measurement chemical properties of soil were conducted at 40 points, these properties including content of Na+, Ca2+, Mg2+, EC, pH and CO32-. Also from 20 points were sampled in order to determine content clay of soil. Then Ks measured in 25 points by Ernst method, because high level of groundwater in Minoo Island, Ernst method is a suitable method. Three parameters including pH, Na+ and CO32- were selected as desirable variables to use in MVR and ANN modeling process. In a comparison between performance of MVR and ANN models to predication of Ks it is clearly has presented that efficiency of ANN model is better of MVR model (R2=36 vs R2=15), in other words, ANN model could be estimate Ks 2.4 times better in comparison to MVR model. According to the results, authors not recommended to use of MVR (as a mathematical model) and ANN (as an empirical model) models to estimate of Ks. authors believe because of dynamic ecosystem Minoo Island (recent layers of young soils (Entisol and Inceptisol soil orders), groundwater level oscillating, heterogeneous properties of soil and tidal irrigation), ecosystem of this area is so complex to interpret soil properties of Minoo Island just by modeling. We propose to use these model just as a complementary tool in addition to field and laboratorial measurements. Finally authors believe because of dynamic ecosystem of study area and unclear boundaries between environmental parameters (soil and water properties of Minoo Island), a system based on Fuzzy Logic or expert system (with a knowledge database based on experiences of experts who worked or will work in this area) is a better selection as a complementary tool to help future researchers.

ANN, MVR, Ks, Minoo, Modeling