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Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction
Jafarzadeh, A.A.; Pal, M.; Servati, M.; FazeliFard, M.H. & Ghorbani, M.A.
Abstract
The aim of this study was to compare the
performance of support vector machine and artificial neural
network techniques to predict the soil cation exchange
capacity of an agricultural research station in terms of soil
characteristics (clay, silt, sand, gypsum, organic matter).
The data consist of 380 soil samples collected from different
horizons of 80 soil profiles located in the Khoja
(Khajeh) region of Azerbaijani provinces, Iran. The support
vector machine and artificial neural network models predict
the cation exchange capacity from the above soil characteristics
of the samples. The models’ results are compared
using three criteria, i.e., root-mean-square errors, Nash–
Sutcliffe and the correlation coefficient. A comparison of
support vector machine results with artificial neural network
method indicates that artificial neural network is
better than the support vector machine method in prediction
of the cation exchange capacity.
Keywords
Clay; Khajeh; Modeling; Pedo-transfer function; Sand
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