Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models

Long-term drought forecasts can provide valuable information to help mitigate some of the consequences of drought. Data driven models are suitable forecast tools due to their minimal information requirements and rapid development times. This study compares the effectiveness of five data driven models for forecasting long-term (6 and 12 months lead time) drought conditions in the Awash River Basin of Ethiopia. The Standard Precipitation Index (SPI 12 and SPI 24) was forecasted using a traditional stochastic model (ARIMA) and compared to machine learning techniques such as artificial neural networks (ANNs), and support vector regression (SVR). In addition to these three model types, wavelet transforms were used to pre-process the inputs for ANN and SVR models to form WA-ANN and WA-SVR models; this is the first time that WA-SVR models have been explored and tested for long-term SPI forecasting. The performances of all models were compared using RMSE, MAE, R2 and a measure of persistence. The forecast results indicate that the coupled wavelet neural network (WA-ANN) models were better than all the other models in this study for forecasting SPI 12 and SPI 24 values over lead times of 6 and 12 months in the Awash River Basin.