ESTIMATING CUMULATIVE INFILTRATION USING ARTIFICIAL NEURAL NETWORKS IN CALCAREOUS SOILS

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.

PARCHAMI ARAGHI F.; MIRLATIFI S.M.; GHORBANI DASHTAKI SH.; MAHDIAN M.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 Year:2010 | Volume:24 | Issue:3 Page(s): 512-526

Download Full-Text

Persian Verion

View:

804

Download:

Cites:

Information Journal Paper

Title

ESTIMATING CUMULATIVE INFILTRATION USING ARTIFICIAL NEURAL NETWORKS IN CALCAREOUS SOILS

Author(s)

PARCHAMI ARAGHI F. | MIRLATIFI S.M. | GHORBANI DASHTAKI SH. | MAHDIAN M.H. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKSQ2

CUMULATIVE INFILTRATIONQ2

INFILTRATION PROCESSQ2

MULTILAYER PERCEPTRONQ2

Abstract

Infiltration process is one of the most important components of the hydrological cycle. On the other hand, the direct measurement of INFILTRATION PROCESS is laborious, time consuming and expensive. In this study, the possibility of predicting CUMULATIVE INFILTRATION in specific time intervals, using readily available soil data and ARTIFICIAL NEURAL NETWORKS (ANNs) was investigated. For this purpose, 210 double ring infiltration data were collected from different regions of Iran. Basic soil properties of the two upper pedogenic layers including initial water content, bulk density, particle-size distributions, organic carbon, gravel content (>2mm size), CaCO3 percent and soil water contents at field capacity and permanent wilting point were determined on each soil sample. The feedforward MULTILAYER PERCEPTRON was used for predicting the CUMULATIVE INFILTRATION at times 5, 10, 15, 20, 30, 45, 60, 90, 120, 150, 180, 210, 240, 270 minutes after the start of the infiltration test and the time of basic infiltration rate. The developed ANNs were categorized into two groups; type 1 and type 2 ANNs. For developing type 1 ANNs, the basic soil properties of the first upper soil horizon were used as inputs, hierarchically. In developing the type 2 ANNs, the available soil properties of the two upper soil horizons were used as inputs, using principal component analysis technique. Results of Reliability test for developed ANNs indicated that type 1 ANNs with a RMSE of 1.136 to 9.312 cm had the best performance in estimating the CUMULATIVE INFILTRATION. Also, type 1 ANNs with the mean RMSD of 6.307 cm had the best performance in estimating the CUMULATIVE INFILTRATION curve.

Cites

THE COMPARISON OF INFILTRATION MODELS IN ORDER TO DETERMINE THE BEST ESTIMATORS OF INFILTRATION RATE IN THE RANGELAND AND WOODLAND LAND USES (CASE STUDY: DAVOOD RASHID WATERSHED, LORESTAN PROVINCE)

References

No record.

Cite

APA: Copy

PARCHAMI ARAGHI, F., MIRLATIFI, S.M., GHORBANI DASHTAKI, SH., & MAHDIAN, M.H.. (2010). ESTIMATING CUMULATIVE INFILTRATION USING ARTIFICIAL NEURAL NETWORKS IN CALCAREOUS SOILS. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), 24(3), 512-526. SID. https://sid.ir/paper/141514/en

Vancouver: Copy

PARCHAMI ARAGHI F., MIRLATIFI S.M., GHORBANI DASHTAKI SH., MAHDIAN M.H.. ESTIMATING CUMULATIVE INFILTRATION USING ARTIFICIAL NEURAL NETWORKS IN CALCAREOUS SOILS. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY)[Internet]. 2010;24(3):512-526. Available from: https://sid.ir/paper/141514/en

IEEE: Copy

F. PARCHAMI ARAGHI, S.M. MIRLATIFI, SH. GHORBANI DASHTAKI, and M.H. MAHDIAN, “ESTIMATING CUMULATIVE INFILTRATION USING ARTIFICIAL NEURAL NETWORKS IN CALCAREOUS SOILS,” JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), vol. 24, no. 3, pp. 512–526, 2010, [Online]. Available: https://sid.ir/paper/141514/en

Related Journal Papers