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Title: Drought forecasting using artificial neural networks and time series of drought indices.
Authors: Morid, Saeid; Smakhtin, Vladimir; Bagherzadeh, K.
Abstract: The article focuses on the study aims to describe the drought forecasting with the use of Artificial Neural Network (ANN) and to predict quantitative values of drought indices which measure the degree of dryness of any time period in Iran. The indices serve as Effective Drought Index (EDI) and the Standard Precipitation Index (SPI). Moreover, there are two predicants most commonly used in medium-range climate forecasting such as El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) indices. Thus, the study found that the final forecasting models can be utilized by drought early warning systems.
Subjects: IRAN; DROUGHT forecasting; ENVIRONMENTAL indicators; ARTIFICIAL neural networks; CLIMATE change research; NORTH Atlantic oscillation; NATURAL disaster warning systems; QUANTITATIVE research; CLIMATOLOGY
Publication: International Journal of Climatology, 2007, Vol 27, Issue 15, p2103
ISSN: 0899-8418
Publication type: Academic Journal
DOI: 10.1002/joc.1498

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Drought forecasting using artificial neural networks and time series of drought indices.