文献类型：期刊文献

英文题名：Spatial-temporal Offshore Current Field Forecasting Using Residual-learning Based Purely CNN Methodology with Attention Mechanism

作者：Zhang, Zeguo[1];Yin, Jianchuan[1]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, 5 Haibin Ave Middle, Zhanjiang 524088, Peoples R China

年份：2024

卷号：38

期号：1

外文期刊名：APPLIED ARTIFICIAL INTELLIGENCE

收录：SCI-EXPANDED(收录号：WOS:001178098600001)、、EI(收录号：20241015686770)、Scopus(收录号：2-s2.0-85186562282)、WOS

基金：This work was supported by the National Natural Science Foundation of China under Grants 52271361 and 52231014, the Special Projects of Key Areas for Colleges and Universities in Guangdong Province under Grant 2021ZDZX1008, and the Natural Science Foundation of Guangdong Province of China under Grant 2023A1515010684.

语种：英文

外文关键词：Climate change - Convergence of numerical methods - Ecosystems - Heat flux - Learning systems - Ocean engineering - Oceanography - Offshore oil well production - Recurrent neural networks - Surface waters

外文摘要：Spatial-temporal current forecasting is indispensable for ocean engineering and marine science exploration, for instance aiding in the conservation and protection of marine ecosystems, planning shipping-routes and determining the length and fuel consumption of sea-going voyages, obtaining deeper insights into the distribution of heat flux within the ocean, which is vital for better understanding climate changes, and so on. Most present related-studies primarily focused on single location or grid-cell-based forecasting, such methodologies are site-specific and neglect the importance of spatial-temporal fidelity. Furtherly, the Recurrent Neural Networks-based methods previously employed exhibit low efficiency in terms of model convergence concerning practical engineering purposes, and numerical weather models are time-consuming and computational expensive. A newly improved Unet-based model using residual-learning with attention strategy is proposed for 2D sea surface current (SSC) velocity predictions with a more efficient perspective. Several machine-learning methodologies were adopted for a better performance comparison. The final predictions demonstrated its superiorities that the proposed neural-learning method outperforms the other established approaches with spatial-resolved mean RMSE less than 0.009 m/s and 0.006 m/s. As a promising surrogate for SSC predictions, the proposed methodology has strong potential in operation marine monitoring and engineering constructions.