AU - V. R. Jalali,
AU - M. Homaee,
TI - Introducing a Nonparametric Model Using k-Nearest Neighbor Technique for Predicting Soil Bulk Density
PT - JOURNAL ARTICLE
TA - JSTNAR
JN - JSTNAR
VO - 15
VI - 56
IP - 56
4099 - http://jstnar.iut.ac.ir/article-1-1717-en.html
4100 - http://jstnar.iut.ac.ir/article-1-1717-en.pdf
SO - JSTNAR 56
ABĀ - Soil bulk density measurements are often required as an input parameter for models that predict soil processes. Nonparametric approaches are being used in various fields to estimate continuous variables. One type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-NN) algorithm was introduced and tested to estimate soil bulk density from other soil properties, including soil textural fractions, EC, pH, SP, OC and TNV. As many as eight nearest neighbors, based on cross validation technique were selected to perform bulk density prediction from the attributes of 136 soil samples. The nonparametric k-NN technique mostly performed equally well using Pearson correlation coefficient (r=0.86), root-mean-squared errors (RMSE=2.5) maximum error (ME=0.15), coefficient of determination (CD=1.3), modeling efficiency (EF=0.75) and coefficient of residual mass (CRM=0.001) statistics. It can be concluded that the k-NN technique is an alternative to other techniques such as pedotransfer functions (PTFs).
CP - IRAN
IN -
LG - eng
PB - JSTNAR
PG - 181
PT - Research
YR - 2011