INTRODUCING A NONPARAMETRIC MODEL USING K-NEAREST NEIGHBOR TECHNIQUE FOR PREDICTING SOIL BULK DENSITY

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

JALALI V.R.; HOMAEE M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Water and Soil Science Year:2011 | Volume:15 | Issue:56 Page(s): 181-190

Download Full-Text

Persian Verion

View:

780

Download:

Cites:

Information Journal Paper

Title

INTRODUCING A NONPARAMETRIC MODEL USING K-NEAREST NEIGHBOR TECHNIQUE FOR PREDICTING SOIL BULK DENSITY

Author(s)

JALALI V.R. | HOMAEE M. | Issue Writer Certificate

Keywords

K-NEAREST NEIGHBOR (K-NN)Q2

SOIL BULK DENSITYQ2

MODELINGQ2

Abstract

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).

Cites

No record.

References

No record.

Cite

APA: Copy

JALALI, V.R., & HOMAEE, M.. (2011). INTRODUCING A NONPARAMETRIC MODEL USING K-NEAREST NEIGHBOR TECHNIQUE FOR PREDICTING SOIL BULK DENSITY. WATER AND SOIL SCIENCE (JOURNAL OF SCIENCE AND TECHNOLOGY OF AGRICULTURE AND NATURAL RESOURCES), 15(56), 181-190. SID. https://sid.ir/paper/14955/en

Vancouver: Copy

JALALI V.R., HOMAEE M.. INTRODUCING A NONPARAMETRIC MODEL USING K-NEAREST NEIGHBOR TECHNIQUE FOR PREDICTING SOIL BULK DENSITY. WATER AND SOIL SCIENCE (JOURNAL OF SCIENCE AND TECHNOLOGY OF AGRICULTURE AND NATURAL RESOURCES)[Internet]. 2011;15(56):181-190. Available from: https://sid.ir/paper/14955/en

IEEE: Copy

V.R. JALALI, and M. HOMAEE, “INTRODUCING A NONPARAMETRIC MODEL USING K-NEAREST NEIGHBOR TECHNIQUE FOR PREDICTING SOIL BULK DENSITY,” WATER AND SOIL SCIENCE (JOURNAL OF SCIENCE AND TECHNOLOGY OF AGRICULTURE AND NATURAL RESOURCES), vol. 15, no. 56, pp. 181–190, 2011, [Online]. Available: https://sid.ir/paper/14955/en

Related Journal Papers