Publications by authors named "Qinglin Mao"
Article Synopsis
- The study addresses the need for accurate flood forecasting in regions with limited data, like the Chaersen Basin, by implementing a hybrid model combining deep learning and hydrological simulation.
- It utilizes the Informer model for advanced pattern recognition, trained on the extensive CAMELS dataset, while integrating the WRF-Hydro model and Global Forecast System (GFS) data for physical modeling.
- The results show marked improvements in flood prediction accuracy, as indicated by significant increases in performance metrics like Nash-Sutcliffe Efficiency and Index of Agreement for the years 2015 and 2016, validating the effectiveness of this hybrid approach.
