文献类型：期刊文献

英文题名：A ConvLSTM nearshore water level prediction model with integrated attention mechanism

作者：Yang, Jian[1,2,3];Zhang, Tianyu[1,2,3];Zhang, Junping[1,2,3];Lin, Xun[1,2,3];Wang, Hailong[1,2,3];Feng, Tao[1,2,3]

机构：[1]Guangdong Ocean Univ, Coll Ocean & Meteorol, Zhanjiang, Peoples R China;[2]Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhanjiang, Peoples R China;[3]Guangdong Ocean Univ, Dept Educ Guangdong Prov, Key Lab Climate Resources & Environm Continental S, 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:001334992900001)、、Scopus(收录号：2-s2.0-85207013607)、WOS

基金：The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was jointly funded by independent research project of Southern Ocean Laboratory (Grant number SML2022SP301); National Natural Science Foundation of China (Grant number 42476219); National Natural Science Foundation of China (Grant number 41976200); Innovative Team Plan for Department of Education of Guangdong Province (No. 2023KCXTD015); National Key Research and Development Program of China (Grant number 2022YFC3103104, 2021YFC3101801); Guangdong Science and Technology Plan Project (Observation of Tropical marine environment in Yuexi); Guangdong Ocean University Scientific Research Program (Grant number 060302032106).

语种：英文

外文关键词：attention mechanism; prediction; ConvLSTM; storm surge; artificial intelligence

外文摘要：Nearshore water-level prediction has a substantial impact on the daily lives of coastal residents, fishing operations, and disaster prevention and mitigation. Compared to the limitations and high costs of traditional empirical forecasts and numerical models for nearshore water-level prediction, data-driven artificial intelligence methods can more efficiently predict water levels. Attention mechanisms have recently shown great potential in natural language processing and video prediction. Convolutional long short-term memory(ConvLSTM) combines the advantages of convolutional neural networks (CNN) and long short-term Memory (LSTM), enabling more effective data feature extraction. Therefore, this study proposes a ConvLSTM nearshore water level prediction model that incorporates an attention mechanism. The ConvLSTM model extracts multiscale information from historical water levels, and the attention mechanism enhances the importance of key features, thereby improving the prediction accuracy and timeliness. The model structure was determined through experiments and relevant previous studies using five years of water level data from the Zhuhai Tide Station and the surrounding wind speed and rainfall data for training and evaluation. The results indicate that this model outperforms the four other baseline models of PCCs, RMSE, and MAE, effectively predicting future water levels at nearshore stations up to 48 h in advance. Compared to the ConvLSTM model, the model with the attention mechanism showed an average improvement of approximately 10% on the test set, with a greater error reduction in short-term forecasts than that in long-term forecasts. During Typhoon Higos, the model reduced the MAE of the best-performing baseline model by approximately 3.2 and 2.4 cm for the 6- and 24-hour forecasts, respectively, decreasing forecast errors by approximately 18% and 11%, effectively enhancing the ability of the model to forecast storm surges. This method provides a new approach for forecasting nearshore tides and storm surges.