深度学习在印度洋偶极子预测中的应用研究综述

郑梦轲; 方巍; 张霄智

doi:10.3969/j.issn.1001-909X.2024.03.004

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海洋学研究 ›› 2024, Vol. 42 ›› Issue (3) : 51-63. DOI: 10.3969/j.issn.1001-909X.2024.03.004

研究综述

深度学习在印度洋偶极子预测中的应用研究综述

郑梦轲 ¹ ,
方巍 ²^,³^,⁴^,^* ,
张霄智 ²^,³^,⁴

作者信息 +

Review of application of deep learning in Indian Ocean Dipole prediction

ZHENG Mengke ¹ ,
FANG Wei ²^,³^,⁴^,^* ,
ZHANG Xiaozhi ²^,³^,⁴

Author information +

文章历史 +

摘要

印度洋偶极子(Indian Ocean Dipole,IOD)是影响区域及全球气候变化的关键气候现象。准确预测IOD对于理解全球气候至关重要,但传统方法在捕捉其复杂性和非线性方面的局限限制了预测能力。该文首先概述了IOD的相关理论,并评估了传统预测方法的优缺点。然后,综合分析了深度学习在IOD预测领域的应用和发展,特别强调了深度学习模型在自动特征提取、非线性关系建模和大数据处理方面相较于传统方法的优势。与此同时,该文还讨论了深度学习模型在IOD预测中所面临的挑战,包括数据稀缺、过拟合以及模型可解释性等问题,并提出了未来研究的方向,旨在推动深度学习技术在气候预测领域的创新与进步。

Abstract

The Indian Ocean Dipole (IOD) is a pivotal climate phenomenon in the Indian Ocean region, exerting a significant impact on the climate change of the surrounding areas and the global climate system. Accurate prediction of IOD is essential for comprehending the dynamics of the global climate, yet traditional forecasting methods are limited in capturing its complexity and nonlinearity, constraining predictive capabilities. This paper begins by outlining the relevant theories of IOD and evaluates the strengths and weaknesses of traditional forecasting methods. It then provides a comprehensive analysis of the application and development of deep learning in the field of IOD prediction, highlights the advantages of deep learning models over traditional methods in terms of automatic feature extraction, nonlinear relationship modeling, and large data processing capabilities. Additionally, the paper discusses the challenges faced by deep learning models in IOD forecasting: including data scarcity, overfitting, and model interpretability issues, and proposes future research directions to promote innovation and progress in the application of deep learning technology in the field of climate prediction.

导出引用

郑梦轲, 方巍, 张霄智. 深度学习在印度洋偶极子预测中的应用研究综述[J]. 海洋学研究. 2024, 42(3): 51-63 https://doi.org/10.3969/j.issn.1001-909X.2024.03.004

ZHENG Mengke, FANG Wei, ZHANG Xiaozhi. Review of application of deep learning in Indian Ocean Dipole prediction[J]. Journal of Marine Sciences. 2024, 42(3): 51-63 https://doi.org/10.3969/j.issn.1001-909X.2024.03.004

中图分类号： P732

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

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印度洋偶极子是热带印度洋中重要的年际变率之一，对印度洋周边国家乃至全球的气候有着重要的影响，关于其形成机制及气候影响的研究对于气候预测具有重要意义。主要回顾了近10年印度洋偶极子的相关研究进展，包括印度洋偶极子的基本特征、与热带太平洋中厄尔尼诺—南方涛动之间的关系、与亚洲夏季风之间的关系、对全球气候的影响以及全球变暖背景下的变化等。印度洋偶极子与热带太平洋中厄尔尼诺—南方涛动之间的关系体现为二者之间是相互影响的，但不同类型的印度洋偶极子对热带太平洋中厄尔尼诺—南方涛动的影响机制尚不明确，还需进一步的研究。印度洋偶极子与亚洲夏季风之间的关系体现为二者之间存在强烈的相互作用，印度洋偶极子与印度洋东部夏季风环流之间存在相互促进作用，而印度洋偶极子与印度夏季风环流之间的相互作用尚需进一步研究。此外，研究表明全球变暖背景下极端正印度洋偶极子的发生将增多，同时极端印度洋偶极子对我国极端气候事件的发生有着重要影响。以往的研究主要集中于单独的印度洋偶极子或印度洋偶极子和热带太平洋中厄尔尼诺—南方涛动的结合对我国极端气候的影响，而印度洋偶极子与中高纬环流系统或泛热带海洋之间的协同作用对我国极端气候事件的影响还亟需相关研究。对印度洋偶极子的系统性回顾可为未来印度洋偶极子的研究提供一定的科学基础。

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曾春艳, 严康, 王志锋, 等. 深度学习模型可解释性研究综述[J]. 计算机工程与应用, 2021, 57(8):1-9.

https://doi.org/10.3778/j.issn.1002-8331.2012-0357

摘要

深度学习技术以数据驱动学习的特点，在自然语言处理、图像处理、语音识别等领域取得了巨大成就。但由于深度学习模型网络过深、参数多、复杂度高等特性，该模型做出的决策及中间过程让人类难以理解，因此探究深度学习的可解释性成为当前人工智能领域研究的新课题。以深度学习模型可解释性为研究对象，对其研究进展进行总结阐述。从自解释模型、特定模型解释、不可知模型解释、因果可解释性四个方面对主要可解释性方法进行总结分析。列举出可解释性相关技术的应用，讨论当前可解释性研究存在的问题并进行展望，以推动深度学习可解释性研究框架的进一步发展。

ZENG

C Y

, YAN

, WANG

Z F

, et al. Survey of interpretability research on deep learning models[J]. Computer Engineering and Applications, 2021, 57(8): 1-9.

https://doi.org/10.3778/j.issn.1002-8331.2012-0357

本文引用 [1] 摘要

With the characteristics of data-driven learning, deep learning technology has made great achievements in the fields of natural language processing, image processing, and speech recognition. However, due to the deep learning model featured by deep networks, many parameters, high complexity and other characteristics, the decisions and intermediate processes made by the model are difficult for humans to understand. Therefore, exploring the interpretability of deep learning has become a new topic in the current artificial intelligence field. This review takes the interpretability of deep learning models as the research object and summarizes its progress. Firstly, the main interpretability methods are summarized and analyzed from four aspects：self-explanatory model, model-specific explanation, model-agnostic explanation, and causal interpretability. At the same time, it enumerates the application of interpretability related technologies, and finally discusses the existing problems of current interpretability research to promote the further development of the deep learning interpretability research framework.

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