Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction

International Journal of Environmental Science and Technology

Volumn 13, Issue 1, 2016, Pages 87-96

  Jafarzadeh, A A ^a   Pal, M ^b   Servati, M ^c   FazeliFard, M H ^a   Ghorbani, M A ^a  

^a UNIVERSITY OF TABRIZ   (Iran)

^b NATIONAL INSTITUTE OF TECHNOLOGY   (India)

^c URMIA UNIVERSITY   (Iran)

Author keywords

Clay; Khajeh; Modeling; Pedo transfer function; Sand

Indexed keywords

AGRICULTURAL MACHINERY; CLAY; FORECASTING; MEAN SQUARE ERROR; MODELS; NEURAL NETWORKS; POSITIVE IONS; SAND; SOILS; SUPPORT VECTOR MACHINES; VECTORS;

ARTIFICIAL NEURAL NETWORK METHODS; ARTIFICIAL NEURAL NETWORK MODELS; CATION EXCHANGE CAPACITIES; CORRELATION COEFFICIENT; KHAJEH; PEDO-TRANSFER FUNCTIONS; ROOT MEAN SQUARE ERRORS; SUPPORT VECTOR MACHINE METHOD;

SOIL SURVEYS;

ARTIFICIAL NEURAL NETWORK; CATION EXCHANGE CAPACITY; CLAY SOIL; COMPARATIVE STUDY; MODELING; PEDOTRANSFER FUNCTION; PREDICTION; SAND; SOIL CHEMISTRY; SUPPORT VECTOR MACHINE;

AZERBAIJAN;

EID: 84952326211     PISSN: 17351472     EISSN: 17352630     Source Type: Journal    
DOI: 10.1007/s13762-015-0856-4     Document Type: Article

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