Regional Storm Surge Forecast Method Based on a Neural Network and the Coupled ADCIRC-SWAN Model

Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas. At present, numerical model forecasting consumes too many resources and takes too long to compute, while neural network forecasting lacks regional data to train regional forecasting models. In this study, we used the DUAL wind model to build typhoon wind fields, and constructed a typhoon database of 75 processes in the northern South China Sea using the coupled Advanced Circulation–Simulating Waves Nearshore (ADCIRC-SWAN) model. Then, a neural network with a Res-U-Net structure was trained using the typhoon database to forecast the typhoon processes in the validation dataset, and an excellent storm surge forecasting effect was achieved in the Pearl River Estuary region. The storm surge forecasting effect of stronger typhoons was improved by adding a branch structure and transfer learning.