TY  - JOUR
T1  - Evaluation of Artificial Neural Network Models for Prediction of Spatial Variability of Some Soil Chemical Properties
AU - Saffari, Mahboub AU - Yasrebi, Jafar AU - Sarikhani, Farkhonde AU - Gazni, Reza AU - Moazallahi, Masome AU - Fathi, Hamed AU - Emadi, Mostafa 
JO  - Research Journal of Biological Sciences
VL  - 4
IS  - 7
SP  - 815
EP  - 820
PY  - 2009
DA  - 2001/08/19
SN  - 1815-8846
DO  - rjbsci.2009.815.820
UR  - https://makhillpublications.co/view-article.php?doi=rjbsci.2009.815.820
KW  - Artificial Neural Network (ANN)
KW  -soil chemical properties
KW  -multiple hidden layers
KW  -evaluation
KW  -Iran
AB  - 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.
ER  -