Artificial neural network approach for predicting cation exchange capacity in soil based on physico-chemical properties

Environmental Engineering Science

Volumn 26, Issue 1, 2009, Pages 137-146

  Tang, Lin ^a   Zeng, Guongming ^a   Nourbakhsh, Farshid ^b   Shen, Guoli L ^a  

^a HUNAN UNIVERSITY   (China)

^b ISFAHAN UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Cation exchange capacity; Physico chemical properties; Radial basis function neural networks; Sensitivity analysis; Soil horizons

Indexed keywords

ARTIFICIAL NEURAL NETWORKS; BUFFERING CAPACITIES; CATION EXCHANGE CAPACITY; CLAY CONTENTS; CORRELATION COEFFICIENTS; DATA SIMULATIONS; EXTERNAL-; GEOGRAPHIC CONDITIONS; INPUT VARIABLES; MEASURED VALUES; MULTIPLE LINEAR REGRESSIONS; NATURAL SOILS; ORGANIC CARBON CONTENTS; PHYSICO-CHEMICAL PROPERTIES; QUANTITATIVE STUDIES; RADIAL BASIS FUNCTION NEURAL NETWORKS; ROBUST METHODS; SAND CONTENTS; SOIL HORIZONS; SOIL SAMPLES; SUBSURFACE LAYERS; TEST SAMPLES;

ATTITUDE CONTROL; BACKPROPAGATION; BIOLOGICAL MATERIALS; CHEMICAL ANALYSIS; CHEMICAL PROPERTIES; CLAY MINERALS; CORRELATION METHODS; FEEDFORWARD NEURAL NETWORKS; FIRE HAZARDS; FUNCTIONS; GEOLOGIC MODELS; IMAGE SEGMENTATION; ION EXCHANGERS; LEACHING; METROPOLITAN AREA NETWORKS; ORGANIC CARBON; POSITIVE IONS; PROBABILITY DENSITY FUNCTION; RADIAL BASIS FUNCTION NETWORKS; SENSITIVITY ANALYSIS; SOIL SURVEYS; SOIL TESTING; SOILS; VEGETATION;

NEURAL NETWORKS;

EID: 58349119422     PISSN: 10928758     EISSN: None     Source Type: Journal    
DOI: 10.1089/ees.2007.0238     Document Type: Article

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