An election algorithm combined with support vector regression for estimating hydrological drought

The prediction of hydrological drought is significant for the management of water resources, planning of hydroelectricity production, agricultural production, habitat, and life of living things. The primary aim of this study is to increase the prediction success hydrological drought in the Wadi Ouahrane basin (270 km2). For this purpose, support vector regression technique is hybridized with Election Algorithm. Standardized Runoff Index (SRI) values were used to determine hydrological droughts. In calculating droughts, rainfall and stream flow data covering the years 1972-2018 were employed. Standardized Precipitation Index (SPI) and previous SRI values were used in the establishment of the hydrological drought prediction model. The coefficient of determination, Standard deviation, the Akaike information criterion and root-mean-square error values were used to test the model accuracy. It was identified to be the most accurate according to the research outputs were obtained with SRI-12 and the Election Algorithm-based support vector regression algorithm showed successful results in hydrological drought prediction. In addition, it was revealed that realistic results are obtained when SPI value and delayed SRI values were used as inputs in multi-time scale analysis of hydrological drought.