PREDICTION OF FC AND PWP USING NEURAL NETWORK AND STATISTICAL REGRESSION IN BARDSIR-KERMAN AREA

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.

SHIRANI H.

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:2012 | Volume:16 | Issue:59 Page(s): 141-151

Download Full-Text

Persian Verion

View:

1,584

Download:

Cites:

Information Journal Paper

Title

PREDICTION OF FC AND PWP USING NEURAL NETWORK AND STATISTICAL REGRESSION IN BARDSIR-KERMAN AREA

Author(s)

SHIRANI H. | Issue Writer Certificate

Keywords

FCQ2

PWPQ2

PEDOTRANSFER FUNCTIONSQ2

BARDSIR-KERMANQ1

Abstract

Field capacity and permasent wilting point are the most important parameters in designing and programming irrigation, whose measurements are troublesome and time-consuming. But these parameters could be estimated by easy data characteristics such as soil texture, organic matter and gypsum, using PEDOTRANSFER FUNCTIONS (PTFs) with high precision. In order to estimate soil moisture at FC and PWP by easy data characteristic, using neural network (ROSETA) and regression models, 20 soil samples with 6 replications were collected from around Bardsir area in Kerman province and the charactersifics including, bulk density, clay, sand, silt, FC, PWP, T.N.V and organic matter were determined for each sample. The results showed that progress in neural network from a low level up to higher level needs new inputs (charactersifics), but without any considerable increase in the precision of prediction. Also, regression analysis for estimation of linear models to predict FC and PWP showed that PWP has a significant positive correlation with clay, and FC significantly correlated with sand, silt and clay. Therefore, two prediction models were constructed for FC and PWP with (R2=69.2) and (R2=76.6), respectively.

Cites

No record.

References

No record.

Cite

APA: Copy

SHIRANI, H.. (2012). PREDICTION OF FC AND PWP USING NEURAL NETWORK AND STATISTICAL REGRESSION IN BARDSIR-KERMAN AREA. WATER AND SOIL SCIENCE (JOURNAL OF SCIENCE AND TECHNOLOGY OF AGRICULTURE AND NATURAL RESOURCES), 16(59), 141-151. SID. https://sid.ir/paper/14628/en

Vancouver: Copy

SHIRANI H.. PREDICTION OF FC AND PWP USING NEURAL NETWORK AND STATISTICAL REGRESSION IN BARDSIR-KERMAN AREA. WATER AND SOIL SCIENCE (JOURNAL OF SCIENCE AND TECHNOLOGY OF AGRICULTURE AND NATURAL RESOURCES)[Internet]. 2012;16(59):141-151. Available from: https://sid.ir/paper/14628/en

IEEE: Copy

H. SHIRANI, “PREDICTION OF FC AND PWP USING NEURAL NETWORK AND STATISTICAL REGRESSION IN BARDSIR-KERMAN AREA,” WATER AND SOIL SCIENCE (JOURNAL OF SCIENCE AND TECHNOLOGY OF AGRICULTURE AND NATURAL RESOURCES), vol. 16, no. 59, pp. 141–151, 2012, [Online]. Available: https://sid.ir/paper/14628/en

Related Journal Papers