USING OF SPECIFIC SURFACE TO IMPROVE THE PREDICTION OF SOIL CEC BY ARTIFICIAL NEURAL NETWORKS

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.

BAYAT H.; DAVATGAR N.; MOALLEMI S.

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

Journal Paper

Paper Information

Journal: WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE) Year:2012 | Volume:21 | Issue:4 Page(s): 105-119

Download Full-Text

Persian Verion

View:

1,005

Download:

Cites:

Information Journal Paper

Title

USING OF SPECIFIC SURFACE TO IMPROVE THE PREDICTION OF SOIL CEC BY ARTIFICIAL NEURAL NETWORKS

Author(s)

BAYAT H. | DAVATGAR N. | MOALLEMI S. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKSQ3

CATION EXCHANGE CAPACITYQ4

PEDOTRANSFER FUNCTIONSQ4

SOIL SPECIFIC SURFACEQ4

Abstract

Cation exchange capacity (CEC) is one of the most important soil properties. Its direct measurement is difficult, costly and time-consuming. In spite of large number of researches done to predict CEC, its prediction improvement by adding new input variables, however, remains a challenging issue. To our knowledge no one has used the auxiliary variable of specific surface to predict CEC. In the present work, 1662 disturbed soil samples were collected from different parts of Guilan province. Soil properties including pH, sand, silt, clay, organic carbon, and CEC were measured. The entire particle size distribution (PSD) curve was extended from limited soil texture data. Using Skaggs et al moded. Then, total specific surface (TSS) and the product of the specific surface of clay fraction and its mass fraction (SS1) were calculated from the extended PSD curve to predict CEC by ARTIFICIAL NEURAL NETWORKS. Strong nonlinear correlation was found between CEC, TSS and SS1. CEC predictions were improved by using TSS and SS1 in the PTFs. SS1 was the most important variable in the prediction of CEC. Partitioning the whole data into eight groups improved significantly the performance of the PTFs and increased the effect of TSS and SS1 in improving the CEC prediction. Using these PTFs is an easy and economical method and it would be a great step forward in improving the estimation of soil CEC.

Cites

No record.

References

Cite

APA: Copy

BAYAT, H., DAVATGAR, N., & MOALLEMI, S.. (2012). USING OF SPECIFIC SURFACE TO IMPROVE THE PREDICTION OF SOIL CEC BY ARTIFICIAL NEURAL NETWORKS. WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE), 21(4), 105-119. SID. https://sid.ir/paper/147659/en

Vancouver: Copy

BAYAT H., DAVATGAR N., MOALLEMI S.. USING OF SPECIFIC SURFACE TO IMPROVE THE PREDICTION OF SOIL CEC BY ARTIFICIAL NEURAL NETWORKS. WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE)[Internet]. 2012;21(4):105-119. Available from: https://sid.ir/paper/147659/en

IEEE: Copy

H. BAYAT, N. DAVATGAR, and S. MOALLEMI, “USING OF SPECIFIC SURFACE TO IMPROVE THE PREDICTION OF SOIL CEC BY ARTIFICIAL NEURAL NETWORKS,” WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE), vol. 21, no. 4, pp. 105–119, 2012, [Online]. Available: https://sid.ir/paper/147659/en

Related Journal Papers