A novel hybrid model for hourly streamflow and water level prediction from radar reflectivity using deep learning approaches

Abstract

Abstract Urban flooding has become an important challenge for metropolitan areas; thus, reliable water level and streamflow predictive models are crucial to flood control and planning. In this study, we develop a hybrid model, namely SGGP, for hourly water level and streamflow predictions in the Jungrang urban basin, located on the Han River, South Korea. This model includes two sub-models in which the first model is established for producing three-hour mean areal precipitation (MAP) from quantitative precipitation forecasts (QPFs) based on the Spatial-scale Decomposition method (SCDM) using Gate Recurrent Units (GRU), and the second model is utilized to predict hourly-ahead water level and streamflow by integrating a GRU with a particle swarm optimization (PSO) algorithm. The radar data, rainfall, water level, and streamflow data were collected from 2008 to 2022, and are used to establish and evaluate the performance of the model. The SGGP model is evaluated using root mean square error (RMSE), correlation coefficient (CC), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and mean absolute percentage error (MAPE) in comparison with four other deep learning models. The results show that the proposed SGGP model achieves accurate results in multistep-ahead water level and streamflow predictions.