Forecasting storm tides during strong typhoons using artificial intelligence and a physical model - Wang, Yulin; Liu, Jingui; Xie, Lingling; Zhang, Tianyu; Wang, Lei (Frontiers in Marine Science, 2024)
The combination of typhoon-induced storm surges and astronomical tides can result in extreme seawater levels and disastrous effects on coastal socioeconomic systems. The construction of an appropriate wind field has consistently been a challenge in storm tide forecasting and disaster warning. In this study, we optimized a nonlinear regression formula based on the C15 model to determine the maximum wind radius. The simulation based on the improvement showed good accuracy for storm tides during super typhoon Mangkhut (WP262018), Saola (WP092023), and severe typhoon Hato (WP152017). The correlation coefficients were in the 0.94–0.98 range, and the peak bias was less than 5cm. The trough errors were significantly reduced compared to other wind fields. Owing to the importance and lack of the maximum wind radius (Rmax), we attempted to predict Rmax using an LSTM (Long Short-Term Memory) neural network for forecasting storm tides during strong typhoons. Constrained LSTM showed good performance in hours 6–48, and effectively enhanced the forecasting capability of storm tides during strong typhoons. The workflows and methods used herein have broad applications in improving the forecasting accuracy of strong typhoon-induced storm tides.