Seasonal Meteorological Drought Prediction Using Support Vector Machine

Document Type : Research Paper


1 Ph.D. of Hydrology and Water Resources, Dept. of Water Sciences and Eng., Sciences and Research Branch, Islamic Azad University, Tehran, Iran

2 Assoc. Prof., of Civil Eng., Tehran University, Tehran

3 Ph.D. Candidate of Water Eng., Dept. of Civil Eng., Tehran University, Tehran


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 October 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 Inverse 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.


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