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<p>Marine ecosystems and coastal economies are seriously threatened by marine heatwaves (MHWs), which are defined as extended periods of abnormally high sea surface temperatures (SSTs). Accurate and early MHW forecasting has become essential because climate change has increased the frequency and severity of such phenomena. In this review, we examine the application of traditional machine learning (ML) and deep learning (DL) methods for MHW detection and prediction. Specifically, we investigate the algorithms (neural networks, ensemble methods, and hybrid architectures) as well as the input variables, datasets, and evaluation metrics employed. Additionally, we review previous studies conducted on different ocean basins to highlight regional patterns and model transferability. Furthermore, we identify the emerging trends in DL, such as the use of explainable artificial intelligence and physics-guided learning for MHW prediction, and outline key challenges and limitations. Finally, we discuss future directions for improving the accuracy, generalization, and interpretability of MHW forecasting systems.</p>

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Machine learning techniques for marine heatwave prediction: a comprehensive review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Suvini Welandawe,
  • Y. H. P. P. Priyadarshana,
  • Nipuna Senanayake,
  • E. Nishchitha S. Silva

摘要

Marine ecosystems and coastal economies are seriously threatened by marine heatwaves (MHWs), which are defined as extended periods of abnormally high sea surface temperatures (SSTs). Accurate and early MHW forecasting has become essential because climate change has increased the frequency and severity of such phenomena. In this review, we examine the application of traditional machine learning (ML) and deep learning (DL) methods for MHW detection and prediction. Specifically, we investigate the algorithms (neural networks, ensemble methods, and hybrid architectures) as well as the input variables, datasets, and evaluation metrics employed. Additionally, we review previous studies conducted on different ocean basins to highlight regional patterns and model transferability. Furthermore, we identify the emerging trends in DL, such as the use of explainable artificial intelligence and physics-guided learning for MHW prediction, and outline key challenges and limitations. Finally, we discuss future directions for improving the accuracy, generalization, and interpretability of MHW forecasting systems.