Short-term and long-term SPI drought forecasts using wavelet neural networks and wavelet support vector regression in the Awash river basin of Ethiopia

Ethiopia's climate variability coupled with the country's heavy reliance on rain-fed agriculture make it vulnerable to the impacts of drought. This vulnerability is evident in the Awash River Basin, where a significant proportion of the population is dependent on international food assistance for survival. Given this vulnerability to drought, effective drought forecasts are an essential tool for effective water resource management as well as mitigation of some of the more adverse consequences of drought. This study forecast the Standard Precipitation Index (SPI) on both short-term and long-term lead times. For short-term forecasts this study computed SPI 1 and SPI 3, short-term drought indicators which represent agricultural drought. For long-term forecasts, SPI 12 and SPI 24 were computed. These two indices are long-term drought indicators which represent hydrological drought conditions.The SPI forecasts were done using five data driven models. Forecasts were compared between two machine learning techniques: artificial neural networks (ANNs) and support vector regression (SVR). The results from these two techniques were compared to a traditional stochastic forecast model, namely an autoregressive integrated moving average (ARIMA) model. In addition, ANN and SVR models were coupled with wavelet analysis (WA) to produce wavelet-neural network (WA-ANN) and wavelet-support vector regression (WA-SVR) models. This study proposed and explored, for the first time, SVR and WA-SVR methods for short term and long term SPI drought forecasting at different lead times.Traditionally, the number of wavelet decompositions of a time series (for forecasting applications) are determined either by trial and error or using the formula L = int[log(N)], with N being the number of samples. This study found that in almost all cases the approximation series after decomposition, and not the detail series, yielded the best forecast results. The decomposition level which had the approximation that yielded the best forecast results was determined to be the appropriate decomposition.With regards to ANN model architecture, traditionally the optimal number of neurons in the hidden layer is either determined using a trial and error procedure, or is determined empirically to be log (N) or 2n+1, where n is the number of input layers. This study combined all these approaches. The empirical methods helped establish upper and lower bounds for the optimal number of neurons within the hidden layer. After an interval was determined, a trial and error procedure was used to determine the optimal number of neurons in the hidden layer.The forecasts in this study were evaluated using a measure of persistence, R2, RMSE, and MAE. The forecast results indicate that WA-ANN and WA-SVR models were the most accurate methods for forecasting the SPI on both short and long-term time scales.

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