Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

The effects of developing technology and rapid population growth on the environment have been expanding gradually. Particularly, in water consumption has revealed necessity management. In this sense, accurate flow estimation is important to Therefore, study, a grey wolf algorithm (GWO)-based gated recurrent unit (GRU) hybrid model proposed for streamflow forecasting. daily data Üçtepe Tuzla observation stations located various collection areas Seyhan basin were utilized. test training analysis models, first 75% used training, remaining 25% testing. accuracy success compared via comparison linear regression, one most basic models artificial neural networks. results analyzed using different statistical indexes. Better obtained GWO-GRU benchmark all metrics except SD at station whole station. At Üçtepe, FMS, despite RMSE MAE being 82.93 85.93 m3/s, was 124.57 it 184.06 m3/s single GRU model. We achieved around 34% 53% improvements, respectively. Additionally, R2 values FMS 0.9827 0.9558 from It observed that could be successfully forecasting studies.