文献类型：期刊文献

英文题名：Developing a deep learning-based storm surge forecasting model

作者：Xie, Wenhong[1];Xu, Guangjun[2,3];Zhang, Hongchun[1];Dong, Changming[1,3,4]

机构：[1]Nanjing Univ Informat Sci & Technol, Nanjing 210044, Peoples R China;[2]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[3]Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519000, Peoples R China;[4]Nanjing Univ Informat Sci & Technol, UNIVER NUIST Joint AI Oceanog Acad, Nanjing 210044, Peoples R China

年份：2023

卷号：182

外文期刊名：OCEAN MODELLING

收录：SCI-EXPANDED(收录号：WOS:000990836300001)、、EI(收录号：20230913634592)、Scopus(收录号：2-s2.0-85148547116)、WOS

基金：Acknowledgments This study is supported by the project supported by Jiangsu Natural Resources Development Special Fund (Marine Science and Technology Innovation) (JSZRHYKJ202102) . This study is supported by Postgrad-uate Research & Practice Innovation Program of Jiangsu Province (KYCX22_1180) . We thank Brandon J. Bethel for his contribution in the introduction section. All authors approved version of the manuscript to be published.

语种：英文

外文关键词：Storm surge; Deep learning; Intelligent forecasting

外文摘要：Storm surge is the anomalous rising of the sea surface induced by intense atmospheric disturbances. The storm surge caused by tropical cyclones often causes great socio-economic, human activity, and life and property hazards to coastal areas. In terms of research resource consumption and computational time, machine learning algorithms that depend on data-driven strong nonlinear mapping skills outperform standard numerical model forecasting. To obtain a lighter and faster storm surge shortcoming forecast, we use a deep learning-based single-station water level prediction model for a storm surge at several locations in this work. In contrast to earlier research, this study employs convolutional neural networks to extract two-dimensional wind field information and merge them with local water level features to produce a more time-efficient intelligent forecast.