RESULTS COMPRESSION OF MAMDANI FUZZY INTERFACE SYSTEM AND ARTIFICIAL NEURAL NETWORKS INT THE SEASONAL RAINFALL PREDICTION, CASE STUDY: KHORASAN REGION

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.

FALLAH GHALHARI GH.A.; MOUSAVI BAYGI M.; HABIBI NOKHANDAN M.

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

Journal Paper

Paper Information

Journal: IRAN-WATER RESOURCES RESEARCH Year:2009 | Volume:5 | Issue:2 (14) Page(s): 40-52

Download Full-Text

Persian Verion

View:

693

Download:

Cites:

Information Journal Paper

Title

RESULTS COMPRESSION OF MAMDANI FUZZY INTERFACE SYSTEM AND ARTIFICIAL NEURAL NETWORKS INT THE SEASONAL RAINFALL PREDICTION, CASE STUDY: KHORASAN REGION

Author(s)

FALLAH GHALHARI GH.A. | MOUSAVI BAYGI M. | HABIBI NOKHANDAN M. | Issue Writer Certificate

Keywords

RAINFALL PREDICTIONQ4

SYNOPTICAL PATTERNSQ4

MAMDANI FUZZY INFERENCE SYSTEMQ4

ARTIFICIAL NEURAL NETWORKQ4

Abstract

Seasonal rainfall forecasts can effectively be used for resources planning and management-e.g., reservoir operations, agricultural practices, and flood emergency responses. Effective planning and management of water resources in the short term requires a general view of the upcoming season. In the long term, this needs realistic projections of scenarios for future variability and changes. In this paper, 33 years of rainfall data in the Khorasan region, northeastern Iran was analyzed. The study area is situated at 31o-38o N, 74o- 80oE. This synoptical data was trained by the MAMDANI FUZZY INFERENCE SYSTEM and the ARTIFICIAL NEURAL NETWORK. For performance evaluation, predicted outputs were compared with the actual rainfall data. First, synoptical relationships were investigated i.e. Sea Level Pressure (SLP), Sea Surface Temperature (SST), Sea Surface Pressure Difference (DSLP), Sea Surface Temperature (DSST) and Air Temperature at 850 hpa, Geopotential Height at 500 hpa, and Relative Humidity at 300 hpa. Models were then calibrated for the period of 1970 to 1992. Finally, the rainfall is predicted. Simulation results revealed that the MAMDANI FUZZY INFERENCE SYSTEM and ARTIFICIAL NEURAL NETWORKs are both promising and efficient. The root mean square for MAMDANI FUZZY INFERENCE SYSTEM and the ARTIFICIAL NEURAL NETWORK were 52 and 41 millimeters, respectively.

Cites

DEVELOPMENT AND COMBINATION OF SOFT COMPUTING AND GEOSTATISTICAL MODELS IN ESTIMATION OF SPATIAL DISTRIBUTION OF GROUNDWATER LEVEL

References

No record.

Cite

APA: Copy

FALLAH GHALHARI, GH.A., MOUSAVI BAYGI, M., & HABIBI NOKHANDAN, M.. (2009). RESULTS COMPRESSION OF MAMDANI FUZZY INTERFACE SYSTEM AND ARTIFICIAL NEURAL NETWORKS INT THE SEASONAL RAINFALL PREDICTION, CASE STUDY: KHORASAN REGION. IRAN-WATER RESOURCES RESEARCH, 5(2 (14)), 40-52. SID. https://sid.ir/paper/100101/en

Vancouver: Copy

FALLAH GHALHARI GH.A., MOUSAVI BAYGI M., HABIBI NOKHANDAN M.. RESULTS COMPRESSION OF MAMDANI FUZZY INTERFACE SYSTEM AND ARTIFICIAL NEURAL NETWORKS INT THE SEASONAL RAINFALL PREDICTION, CASE STUDY: KHORASAN REGION. IRAN-WATER RESOURCES RESEARCH[Internet]. 2009;5(2 (14)):40-52. Available from: https://sid.ir/paper/100101/en

IEEE: Copy

GH.A. FALLAH GHALHARI, M. MOUSAVI BAYGI, and M. HABIBI NOKHANDAN, “RESULTS COMPRESSION OF MAMDANI FUZZY INTERFACE SYSTEM AND ARTIFICIAL NEURAL NETWORKS INT THE SEASONAL RAINFALL PREDICTION, CASE STUDY: KHORASAN REGION,” IRAN-WATER RESOURCES RESEARCH, vol. 5, no. 2 (14), pp. 40–52, 2009, [Online]. Available: https://sid.ir/paper/100101/en

Related Journal Papers