Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression

Authors:
A. Belayneh;J. Adamowski
Affiliations:
Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, McGill University, QC, Canada;Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, McGill University, QC, Canada
Venue:
Applied Computational Intelligence and Soft Computing - Special issue on Applied Neural Intelligence to Modeling, Control, and Management of Human Systems and Environments
Year:
2012

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Abstract

Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of drought. Data-driven models are suitable forecasting tools due to their rapid development times, as well as minimal information requirements compared to the information required for physically based models. This study compares the effectiveness of three data-driven models for forecasting drought conditions in the Awash River Basin of Ethiopia. The Standard Precipitation Index (SPI) is forecast and compared using artificial neural networks (ANNs), support vector regression (SVR), and wavelet neural networks (WN). SPI 3 and SPI 12 were the SPI values that were forecasted. These SPI values were forecast over lead times of 1 and 6 months. The performance of all the models was compared using RMSE, MAE, and R2. The forecast results indicate that the coupled wavelet neural network (WN) models were the best models for forecasting SPI values over multiple lead times in the Awash River Basin in Ethiopia.