BiST-SA-LSTM: A Deep Learning Framework for End-to-End Prediction of Mesoscale Eddy Distribution in Ocean

BiST-SA-LSTM: A Deep Learning Framework for End-to-End Prediction of Mesoscale Eddy Distribution in Ocean

Mesoscale eddies play a critical role in sea navigation and route planning, yet traditional prediction methods have often overlooked their spatial relationships, relying on indirect approaches to capture their distribution across extensive maps. To address this limitation, we present BiST-SA-LSTM, a...

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Bibliographic Details
Main Authors: Yaoran Chen, Zijian Zhao, Yaojun Yang, Xiaowei Li, Yan Peng, Hao Wu, Xi Zhou, Dan Zhang, Hongyu Wei
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Journal of Marine Science and Engineering
Subjects:
mesoscale eddy distribution
spatial temporal prediction
Bi-LSTM
self-attention
multivariable analysis
Online Access:https://www.mdpi.com/2077-1312/13/1/52
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