MODELING OF TABRIZ PLAIN RAINFALL USING 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.

ASGHARI MOGHADAM A.; NOURANI V.; NADIRI A.A.

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

Journal Paper

Paper Information

Journal: JOURNAL OF AGRICULTURAL KNOWLEDGE Year:2008 | Volume:18 | Issue:1 Page(s): 1-15

Download Full-Text

Persian Verion

View:

1,424

Download:

Cites:

Information Journal Paper

Title

MODELING OF TABRIZ PLAIN RAINFALL USING ARTIFICIAL NEURAL NETWORKS

Author(s)

ASGHARI MOGHADAM A. | NOURANI V. | NADIRI A.A. | Issue Writer Certificate

Keywords

ANNSQ3

BLACK BOX MODELQ2

FEED FORWARD NETWORKQ3

RAINFALL MODELINGQ3

Abstract

Fast spreading of using artificial neural networks (ANNS), black box and qualified models, in different sciences such as hydrology shows its studying necessity and values. The most important applications of the model in hydrology are water quality modeling and prediction, optimization, classification, estimation of hydrological phenomena and parameters. The aim of this paper was to provide application of artificial neural networks, empirical equation of ascertaining hidden nodes and discussing their strengths and limitations for presenting rainfall artificial neural network forecasting model of Tabriz plain area. In this modeling six different structures of ANNS were used in which the FEED FORWARD NETWORK with six input nodes and a hidden layer composed the best model. This model was used for showing the effects of learning sample and hidden nodes numbers on minimizing of the model error.

Cites

References

No record.

Cite

APA: Copy

ASGHARI MOGHADAM, A., NOURANI, V., & NADIRI, A.A.. (2008). MODELING OF TABRIZ PLAIN RAINFALL USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF AGRICULTURAL KNOWLEDGE, 18(1), 1-15. SID. https://sid.ir/paper/28654/en

Vancouver: Copy

ASGHARI MOGHADAM A., NOURANI V., NADIRI A.A.. MODELING OF TABRIZ PLAIN RAINFALL USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF AGRICULTURAL KNOWLEDGE[Internet]. 2008;18(1):1-15. Available from: https://sid.ir/paper/28654/en

IEEE: Copy

A. ASGHARI MOGHADAM, V. NOURANI, and A.A. NADIRI, “MODELING OF TABRIZ PLAIN RAINFALL USING ARTIFICIAL NEURAL NETWORKS,” JOURNAL OF AGRICULTURAL KNOWLEDGE, vol. 18, no. 1, pp. 1–15, 2008, [Online]. Available: https://sid.ir/paper/28654/en

Related Journal Papers