Forecasting of Categorical Drought Pattern via Partitioning of Meteorological Variables

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.

Nikbakht Shahbazi a.r.; ZAHRAIE BANAFSHEH

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:2019 | Volume:12 | Issue:40 Page(s): 13-26

Download Full-Text

Persian Verion

View:

882

Download:

Cites:

Information Journal Paper

Title

Forecasting of Categorical Drought Pattern via Partitioning of Meteorological Variables

Author(s)

Nikbakht Shahbazi a.r. | ZAHRAIE BANAFSHEH | Issue Writer Certificate

Keywords

Meteorological DroughtQ2

Standardized Precipitation Index (SPI) ClassificationQ1

Support Vector Machine (SVM)Q1

Tehran’s WatershedsQ1

Abstract

Drought prediction is an important item in realm of hydrometeorology and hydrology, and selection of suitable meteorological variables for drought prediction is a goal in recent studies. In this paper, suitable feature selection is investigated with application of Mutual Information (MI) on the predictor’ s time series and the well-known statistical machine learning methods, Support Vector Machine (SVM), is proposed to predict drought class based on Standardized Precipitation Index (SPI) in some seasonal scale scenario in the main watersheds of Tehran. In current study, ground weather temperature (at 300, 500, 700 and 850 mi bar) and geopotential height (at 300, 500, 700 and 850 mi bar) was applied in prediction models based on data from 1975 to 2005 in the main watershed of Tehran. Regarding to the amount of predictors, suitable feature selection is investigated with application of Mutual Information (MI) on the predictor’ s time series and target time series and the well-known statistical machine learning methods, support vector machine (SVM), is applied to predict SPI class. One of the important issue in this research is use of different variables, for example regarding to selected data points, the effective regions on Tehran precipitation are southern, southwestern and northwestern of Iran in spring, northern and northwestern in autumn and northwestern and western in winter. SVM depicted accurate results in classification and prediction of SPI and it is suitable and applicable. The predicted SPI in winter and autumn are more accurate than the other scenarios.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

Nikbakht Shahbazi, a.r., & ZAHRAIE, BANAFSHEH. (2019). Forecasting of Categorical Drought Pattern via Partitioning of Meteorological Variables. WATER ENGINEERING, 12(40 ), 13-26. SID. https://sid.ir/paper/169499/en

Vancouver: Copy

Nikbakht Shahbazi a.r., ZAHRAIE BANAFSHEH. Forecasting of Categorical Drought Pattern via Partitioning of Meteorological Variables. WATER ENGINEERING[Internet]. 2019;12(40 ):13-26. Available from: https://sid.ir/paper/169499/en

IEEE: Copy

a.r. Nikbakht Shahbazi, and BANAFSHEH ZAHRAIE, “Forecasting of Categorical Drought Pattern via Partitioning of Meteorological Variables,” WATER ENGINEERING, vol. 12, no. 40 , pp. 13–26, 2019, [Online]. Available: https://sid.ir/paper/169499/en

Related Journal Papers