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