Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN)

In this study, the preprocessing of the gamma test was used to select the appropriate input combination into two models including the support vector regression (SVR) model and artificial neural networks (ANNs) to predict the stream flow drought index (SDI) of different timescales (i.e., 3, 6, 9, 12, and 24 months) in Latian watershed, Iran, which is one of the most important sources of water for the large metropolitan Tehran. The variables used included SDI _t , SDI _t ? 1, SDI _t ? 2, SDI _t ? 3, and SDI _t ? 4 monthly delays. Two variables including SDI _t and SDI _t ? 1 with lower gamma values were identified as the most optimal combination of variables in all drought timescales. The results showed that the gamma test was able to correctly identify the right combination for the forecasting of 6, 9, and 12 months SDI using the ANN model. Also, the gamma test was considered in selecting the appropriate inputs for identifying the values of 9, 12, and 24 months SDI in SVR. The support vector machine approach showed a better efficiency in the forecast of long-term droughts compared to the artificial neural network. In total, among forecasts made for 30 scenarios, the support vector machine model only in scenario 3 of SDI₃, scenario 1 of SDI₆, and scenarios 2 and 3 of SDI₂₄ represented poorer efficiency compared to the artificial neural network (MLP layer), but in other scenarios, the results of SVR had better efficiency.