@article{MAKHILLRJBS20094710951,
    title = {Evaluation of Artificial Neural Network Models for Prediction of Spatial Variability of Some Soil Chemical Properties},
    journal = {Research Journal of Biological Sciences},
    volume = {4},
    number = {7},
    pages = {815-820},
    year = {2009},
    issn = {1815-8846},
    doi = {rjbsci.2009.815.820},
    url = {https://makhillpublications.co/view-article.php?issn=1815-8846&doi=rjbsci.2009.815.820},
    author = {Mahboub,Jafar,Farkhonde,Reza,Masome,Hamed and},
    keywords = {Artificial Neural Network (ANN),soil chemical properties,multiple hidden layers,evaluation,Iran},
    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.}
    }