Forecasting of Categorical Drought Pattern via Partitioning of Meteorological Variables

Subject Areas : Article frome a thesis

Alireza Nikbakht Shahbazi ¹ , Banafsheh Zahraie ²

1 - Department of Water Science and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran. Email: [e-mail]
2 - Associate Professor, School of Civil Engineering, Department of Engineering, University of Tehran, Tehran, Iran.

Received: 2016-06-07 Accepted : 2019-06-09 Published : 2019-05-22

Keywords: Meteorological Drought, Standardized Precipitation Index (SPI) Classification, Support Vector Machine (SVM), Tehran’s Watersheds,

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.

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