COMPARISON OF ARTIFICIAL NEURAL NETWORK AND SDSM METHODS IN THE DOWNSCALING OF ANNUAL RAINFALL IN THE HADCM3 MODELLING (CASE STUDY: KERMAN, RAVAR AND RABOR)

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.

REZAEE M.; NAHTANI M.; MOGHADAMNIYA A.R.; ABKAR A.J.; REZAEE M.

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

Journal Paper

Paper Information

Journal: Journal of Water Resources Engineering Year:2015 | Volume:8 | Issue:24 Page(s): 25-40

Download Full-Text

Persian Verion

View:

1,724

Download:

Cites:

Information Journal Paper

Title

COMPARISON OF ARTIFICIAL NEURAL NETWORK AND SDSM METHODS IN THE DOWNSCALING OF ANNUAL RAINFALL IN THE HADCM3 MODELLING (CASE STUDY: KERMAN, RAVAR AND RABOR)

Author(s)

Keywords

PRECIPITATIONQ2

CLIMATE CHANGEQ2

DOWNSCALING HADCM3 MODELQ1

Abstract

Nowadays, it is believed that anthropogenic activities, such as changes in land use and deforestation, have resulted in atmospheric concentrations of greenhouse gases. One consequence of this ruinous activity, is an alteration of the energy balance that tends to warm the atmosphere that has resulted in CLIMATE CHANGE. PRECIPITATION forecast is one of the most important element in water resources management and planning. In this study, PRECIPITATION depth of Kerman, Ravar and Rabor Stations have been predicted using the HadCM3 model outputs under the A2 scenario, SDSM downscaling models and artificial neural network, for three periods: 2010-2039, 2040-2069 and 2070-2099. PRECIPITATION data for the 1971-2001 period were selected as the base one. The results obtained by using the two models were evaluated and compared according to the statistical criteria. The artificial neural network model showed superior performance for the Kerman and Ravar stations. Annual PRECIPITATION of Kerman, Ravar and Rabor stations by 2099, using the AMM model decreases by 12.86, 11.68, and 11.39 percentage points, respectively. These are for 0.89, 18.48, and 1.55 percentage points, respectively, for the same year.

Cites

HYDROLOGICAL IMPACT OF CLIMATE CHANGE ON THE GAVEHRUD WATERSHED AS AFFECTED BY THE PARAMETER UNCERTAINTY

References

Cite

APA: Copy

REZAEE, M., NAHTANI, M., MOGHADAMNIYA, A.R., ABKAR, A.J., & REZAEE, M.. (2015). COMPARISON OF ARTIFICIAL NEURAL NETWORK AND SDSM METHODS IN THE DOWNSCALING OF ANNUAL RAINFALL IN THE HADCM3 MODELLING (CASE STUDY: KERMAN, RAVAR AND RABOR). WATER ENGINEERING, 8(24), 25-40. SID. https://sid.ir/paper/169348/en

Vancouver: Copy

REZAEE M., NAHTANI M., MOGHADAMNIYA A.R., ABKAR A.J., REZAEE M.. COMPARISON OF ARTIFICIAL NEURAL NETWORK AND SDSM METHODS IN THE DOWNSCALING OF ANNUAL RAINFALL IN THE HADCM3 MODELLING (CASE STUDY: KERMAN, RAVAR AND RABOR). WATER ENGINEERING[Internet]. 2015;8(24):25-40. Available from: https://sid.ir/paper/169348/en

IEEE: Copy

M. REZAEE, M. NAHTANI, A.R. MOGHADAMNIYA, A.J. ABKAR, and M. REZAEE, “COMPARISON OF ARTIFICIAL NEURAL NETWORK AND SDSM METHODS IN THE DOWNSCALING OF ANNUAL RAINFALL IN THE HADCM3 MODELLING (CASE STUDY: KERMAN, RAVAR AND RABOR),” WATER ENGINEERING, vol. 8, no. 24, pp. 25–40, 2015, [Online]. Available: https://sid.ir/paper/169348/en

Related Journal Papers