SEASONAL METEOROLOGICAL DROUGHT PREDICTION USING SUPPORT VECTOR MACHINE

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NIKBAKHT SHAHBAZI ALIREZA; ZAHRAIE BANAFSHEH; NASSERI MOHSEN

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Journal: WATER AND WASTEWATER Year:2012 | Volume:23 | Issue:2 (82) Page(s): 72-84

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Information Journal Paper

Title

SEASONAL METEOROLOGICAL DROUGHT PREDICTION USING SUPPORT VECTOR MACHINE

Author(s)

NIKBAKHT SHAHBAZI ALIREZA | ZAHRAIE BANAFSHEH | NASSERI MOHSEN | Issue Writer Certificate

Keywords

CLIMATOLOGICAL DROUGHTQ3

SUPPORT VECTORQ2

STANDARDIZED PRECIPITATION INDEX (SPI)Q3

TEHRAN PROVINCEQ4

Abstract

In various researches, implementation of meteorological parameters in drought prediction is studied. In the current work, meteorological drought classes based on Standardized Precipitation Index (SPI) for six seasonal scenarios (autumn, winter, spring, autumn +winter, winter +spring, and autumn +winter +spring) and meteorological predictors contained ground and sea surface temperature, weather temperature (at 300, 500, 700 and 850 mi bar) and geopotential height (at 300, 500, 700 and 850 mi bar) wide of North (0, 60) and East (0, 90) was applied in prediction models based on data from 1975 to 2005. In these models, temporal range of meteorological predictors is between Octobers to April month on the same predicted SPI. SPI was calculated based on mean precipitation at seasonal time scale in the main watershed of Tehran (Taleghan, Mamloo) by verse Weighted Distance method. The well-known statistical supervised machine learning method, SUPPORT VECTOR machine (SVM), is applied to predict SPI. 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 prediction of SPI, spatially prediction of SPI in all scenarios, and it can be proposed as a very suitable statistical learning method in investigating of nonlinear behavior of meteorological phenomena with a short samples. The predicted SPI in spring and autumn are more accurate than the other scenarios.

Cites

PREDICTION OF SPI DROUGHT INDEX USING SUPPORT VECTOR AND MULTIPLE LINEAR REGRESSIONS

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APA: Copy

NIKBAKHT SHAHBAZI, ALIREZA, ZAHRAIE, BANAFSHEH, & NASSERI, MOHSEN. (2012). SEASONAL METEOROLOGICAL DROUGHT PREDICTION USING SUPPORT VECTOR MACHINE. WATER AND WASTEWATER, 23(2 (82)), 72-84. SID. https://sid.ir/paper/381887/en

Vancouver: Copy

NIKBAKHT SHAHBAZI ALIREZA, ZAHRAIE BANAFSHEH, NASSERI MOHSEN. SEASONAL METEOROLOGICAL DROUGHT PREDICTION USING SUPPORT VECTOR MACHINE. WATER AND WASTEWATER[Internet]. 2012;23(2 (82)):72-84. Available from: https://sid.ir/paper/381887/en

IEEE: Copy

ALIREZA NIKBAKHT SHAHBAZI, BANAFSHEH ZAHRAIE, and MOHSEN NASSERI, “SEASONAL METEOROLOGICAL DROUGHT PREDICTION USING SUPPORT VECTOR MACHINE,” WATER AND WASTEWATER, vol. 23, no. 2 (82), pp. 72–84, 2012, [Online]. Available: https://sid.ir/paper/381887/en

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