文献类型：期刊文献

英文题名：Forecasting storm tides during strong typhoons using artificial intelligence and a physical model

作者：Wang, Yulin[1];Liu, Jingui[1,2,3,4];Xie, Lingling[1,3,4,5];Zhang, Tianyu[1,2,3,4,5];Wang, Lei[2]

机构：[1]Guangdong Ocean Univ, Coll Ocean & Meteorol, Lab Coastal Ocean Variat & Disaster Predict, Zhanjiang, Peoples R China;[2]Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai, Peoples R China;[3]Guangdong Ocean Univ, Guangdong Key Lab Climate Resource & Environm Cont, Zhanjiang, Peoples R China;[4]Minist Nat Resources, Key Lab Space Ocean Remote Sensing & Applicat, Zhanjiang, Peoples R China;[5]Guangdong Ocean Univ, Guangdong Western Trop Marine Ecol & Environm Fiel, Zhanjiang, Peoples R China

年份：2024

卷号：11

外文期刊名：FRONTIERS IN MARINE SCIENCE

收录：SCI-EXPANDED(收录号：WOS:001281378400001)、、Scopus(收录号：2-s2.0-85200035826)、WOS

基金：The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by Independent Research Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), China (SML2022SP301), National Natural Science Foundation of China (42276019, 41976018), Program for Scientific Research Start-up Funds of Guangdong Ocean University (060302032106, 060302032202).

语种：英文

外文关键词：storm tides; largest wind radius; parametric wind field; artificial intelligence; ADCIRC

外文摘要：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.