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Drought is a natural creeping threat with numerous damaging effects invarious aspects of human life. Accurate drought prediction is a promising stepin helping policy makers to set drought risk management strategies. To fulfillthis purpose, choosing appropriate models plays an important role in predictingapproach. In this study, different models of Artificial Neural Network (ANN)are employed to predict short and long-term of droughts by using StandardizedPrecipitation Index (SPI) at different time scales, including 3, 6, 12, 24 and48 months in Tabriz city, Iran. To this end, different combination ofcalculated SPI and time series of various hydro-meteorological variables, suchas precipitation, wind velocity, relative humidity and sunshine hours for years1992 to 2010 are used to train the ANN models. In order to compare the modelsperformances, some well-known measures, namely RMSE, Mean Absolute Error (MAE)and Correlation Coefficient (CC) are utilized in the present study. The resultsillustrate that the application of all hydro-meteorological variablessignificantly improves the prediction of SPI at different time scales.

Prediction of short and long-term droughts using artificial neural networks and hydro-meteorological variables