Authors : Mahboub Saffari, Jafar Yasrebi, Farkhonde Sarikhani, Reza Gazni, Masome Moazallahi, Hamed Fathi and Mostafa Emadi
Abstract: Analysis and interpretation of spatial variability of soils properties is a keystone in site-specific management. The objectives of this study were to evaluate two different Artificial Neural Network (ANN) structures as single hidden-layer and multiple hidden-layer for estimation of spatial variability of some soil chemical properties. Soil samples were collected at approximately 60x60 m grids at 0-30 cm depth and coordinates of each of the 100 points were recorded with GPS. ANN models, applicable to each of these soils and consisting of two input parameters (X and Y coordinate system) were developed. The whole data is composed of 100 data points, which separated into two parts randomly: A training set consisting of 80% data points and a validation or testing set consisting of 20% data points. Generally, approximately the study highlights the superiority of the multiple hidden layers ANN model over single hidden layer ANN models (except Ca), for determining soil properties compacted to a given state.
Mahboub Saffari, Jafar Yasrebi, Farkhonde Sarikhani, Reza Gazni, Masome Moazallahi, Hamed Fathi and Mostafa Emadi, 2009. Evaluation of Artificial Neural Network Models for Prediction of Spatial Variability of Some Soil Chemical Properties. Research Journal of Biological Sciences, 4: 815-820.