用于加强短期和中期海面温度预测的协调注意残余 U-Net 模型

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Zhao Sun, Yongxian Wang
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引用次数: 0

摘要

海洋表面温度(SST)对于研究全球海洋和评估生态系统至关重要。准确预测短期和中期的日 SST 一直是海洋学领域的重大挑战。传统的深度学习方法可以处理时间数据和空间特征,但在处理长程时空依赖性时往往力不从心。针对这一问题,我们提出了一种协调注意残差 U-Net 模型(CResU-Net),旨在更好地捕捉高分辨率 SST 的动态时空相关性。该模型整合了协调注意机制、多个残差模块和深度可分离卷积,以增强预测能力。南海不同区域的 SST 时空变化非常复杂,因此准确预测具有挑战性。在南海不同区域进行的实验表明,该模型在预测高分辨率日海温方面效果显著,并具有强大的泛化能力。在 10 天的预报期内,该模式的均方根误差(RMSE)约为 0.3 ℃,优于多个先进模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A coordination attention residual U-Net model for enhanced short and mid-term sea surface temperature prediction
Sea surface temperature (SST) is crucial for studying global oceans and evaluating ecosystems. Accurately predicting short and mid-term daily SST has been a significant challenge in oceanography. Traditional deep learning methods can handle temporal data and spatial features but often struggle with long-range spatiotemporal dependencies. To address this, we propose a coordination attention residual U-Net(CResU-Net) model designed to better capture the dynamic spatiotemporal correlations of high-resolution SST. The model integrates coordinate attention mechanisms, multiple residual modules, and depthwise separable convolutions to enhance prediction capabilities. The spatiotemporal variations of SST across different areas of the South China Sea are complex, making accurate predictions challenging. Experiments across various regions of the South China Sea show the model’s effectiveness and robust generalization in predicting high-resolution daily SST. For a 10-day forecast period, the model achieves approximately 0.3 °C in RMSE, outperforming several advanced models.
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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