Clustering the rainfall of Iran with using new approach based on Singular Value Decomposition Mapping and Fuzzy C-Means Clustering

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.

RORADEH HEMATOLAH; GHASEMI JAMAL; YOUSEFI YADOLAH; Ghasemi Zohre

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

Journal Paper

Paper Information

Journal: Geographical Planning of Space Year:2019 | Volume:9 | Issue:31 Page(s): 113-124

Download Full-Text

Persian Verion

View:

1,014

Download:

Cites:

Information Journal Paper

Title

Clustering the rainfall of Iran with using new approach based on Singular Value Decomposition Mapping and Fuzzy C-Means Clustering

Author(s)

RORADEH HEMATOLAH | GHASEMI JAMAL | YOUSEFI YADOLAH | Ghasemi Zohre | Issue Writer Certificate

Keywords

precipitationQ2

fuzzy clusteringQ2

SVDQ1

linear and non-linear precipitation featuresQ1

IranQ2

Abstract

The aim of this study is clustering of Iran precipitation in order to recognize its spatial differences. Daily data of precipitation, relative humidity, and due point temperature were acquired from 63 stations since the year 1980 till 2013 by national meteorology organization. These primary data by using of the method Singular Value Decomposition (SVD) mapping were compressed to single values and then decreased and prepared as non-linear properties. In addition to these properties, some other properties such as the days with heavy precipitation (greater than or equal to 10 mm), days with much heavy precipitation (greater than or equal to 20 mm), days with precipitation greater than or equal to 25 mm, greatest number of consecutive dry days (CDD), greatest number of consecutive wet days (CWD), and the number of days with precipitation at every season were acquired as linear properties of daily precipitation. In the final step, these linear and non-linear properties were joined together then entered to the fuzzy clustering system. The result of this study showed six precipitation clusters in Iran. The coastal areas of the Caspian Sea, Persian Gulf and Oman Sea, the central very dry, semi-arid, mountainous and semi-mountainous areas were grouped in definite clusters according to Iran topography, Latitude and distance from the sea and water resources. Comparing the results of clustering by this method with other methods that have been done until now, we found that this new approach of clustering can distinguished the stations and clusters regarding its causative agents, significantly high performance.

Cites

No record.

References

No record.

Cite

APA: Copy

RORADEH, HEMATOLAH, GHASEMI, JAMAL, YOUSEFI, YADOLAH, & Ghasemi, Zohre. (2019). Clustering the rainfall of Iran with using new approach based on Singular Value Decomposition Mapping and Fuzzy C-Means Clustering. GEOGRAPHICAL PLANING OF SPACE, 9(31 ), 113-124. SID. https://sid.ir/paper/251075/en

Vancouver: Copy

RORADEH HEMATOLAH, GHASEMI JAMAL, YOUSEFI YADOLAH, Ghasemi Zohre. Clustering the rainfall of Iran with using new approach based on Singular Value Decomposition Mapping and Fuzzy C-Means Clustering. GEOGRAPHICAL PLANING OF SPACE[Internet]. 2019;9(31 ):113-124. Available from: https://sid.ir/paper/251075/en

IEEE: Copy

HEMATOLAH RORADEH, JAMAL GHASEMI, YADOLAH YOUSEFI, and Zohre Ghasemi, “Clustering the rainfall of Iran with using new approach based on Singular Value Decomposition Mapping and Fuzzy C-Means Clustering,” GEOGRAPHICAL PLANING OF SPACE, vol. 9, no. 31 , pp. 113–124, 2019, [Online]. Available: https://sid.ir/paper/251075/en

Related Journal Papers