Daily runoff prediction during flood seasons based on the VMD–HHO–KELM model

Abstract

Abstract Improving the accuracy of daily runoff in the lower Yellow River is important for flood control and reservoir scheduling in the lower Yellow River. Influenced by factors such as meteorology, climate change, and human activities, runoff series present non-stationary and non-linear characteristics. To weaken the non-linearity and non-smoothness of runoff time series and improve the accuracy of daily runoff prediction, a new combined runoff prediction model (VMD–HHO–KELM) based on the ensemble Variational Modal Decomposition (VMD) algorithm and Harris Hawk Optimisation (HHO) algorithm-optimised Kernel Extreme Learning Machine (KELM) is proposed and applied to Gaocun and Lijin hydrological stations. The VMD–HHO–KELM model has the highest prediction accuracy, with the prediction model R2 reaching 0.95, mean absolute error reaching 13.3, and root mean square error reaching 33.83 at the Gaocun hydrological station, and R2 reaching 0.96, mean absolute error reaching 8.03, and root mean square error reaching 38.45 at the Lijin hydrological station.