海洋学研究 ›› 2024, Vol. 42 ›› Issue (3): 51-63.DOI: 10.3969/j.issn.1001-909X.2024.03.004
收稿日期:
2024-04-01
修回日期:
2024-07-10
出版日期:
2024-09-15
发布日期:
2024-11-25
通讯作者:
*方巍(1975—),男,教授,博士生导师, CCF高级会员,主要从事人工智能、大数据分析、机器学习、计算机视觉等方面的研究,E-mail: hsfangwei@sina.com。
作者简介:
郑梦轲(1997—),男,安徽省合肥市人,主要从事深度学习、大数据分析方面的研究,E-mail: 1428592959@qq.com。
基金资助:
ZHENG Mengke1(), FANG Wei2,3,4,*(), ZHANG Xiaozhi2,3,4
Received:
2024-04-01
Revised:
2024-07-10
Online:
2024-09-15
Published:
2024-11-25
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。
中图分类号:
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.
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.
方法 | 优点 | 局限性 |
---|---|---|
统计模型 | 简易且计算效率高 | 对历史数据的过度依赖使其对未知的气候变化处理困难;无法捕捉复杂的非线性关系;对观测误差敏感 |
数值模式 | 可处理复杂的物理机制和相互作用,有助于理解IOD的物理机制;可提供详细的时空分辨率;可进行长期预测 | 对初始条件和参数化方案敏感;计算复杂度高,计算资源需求大 |
表1 传统IOD预测方法的优缺点分析
Tab.1 Analysis of the advantages and limitations of traditional IOD forecasting methods
方法 | 优点 | 局限性 |
---|---|---|
统计模型 | 简易且计算效率高 | 对历史数据的过度依赖使其对未知的气候变化处理困难;无法捕捉复杂的非线性关系;对观测误差敏感 |
数值模式 | 可处理复杂的物理机制和相互作用,有助于理解IOD的物理机制;可提供详细的时空分辨率;可进行长期预测 | 对初始条件和参数化方案敏感;计算复杂度高,计算资源需求大 |
图3 观测到的偶极子指数(DMI)与卷积神经网络(CNN)和北美多模型集合(NMME)预测结果的对比 (图件改绘自文献[40]。图例中“cor”表示相关系数,“lead”表示模型能够提前预测的时间长度。)
Fig.3 Comparison of the observed dipole mode index (DMI) with the predicted DMI using the convolutional neural network (CNN) and North American Multi-Model Ensemble (NMME) (Figure is redrawn from reference [40]. In legend,‘cor’ refers to the correlation coefficient and ‘lead’ indicates the length of time the model can predict in advance.)
图5 2015—2018年基于前6个月风场数据的ConvLSTM模型和CFSv2模型的IOD指数预测结果对比 (图件改绘自文献[50]。图例中“6u”表示过去6个月的风速数据。)
Fig.5 Comparison of IOD index prediction results from ConvLSTM model and CFSv2 model based on wind field data of the first 6 months from 2015 to 2018 (Figure is redrawn from reference [50]. In legend,“6u” refers to the wind speed data from the past 6 months.)
项目 | 优势 | 局限性 |
---|---|---|
数据处理 | 能够有效地处理和分析大规模的气候数据集,从历史数据中发现不易被传统方法察觉的复杂模式和关联。 | 需要大量计算资源的同时还对大数据集有较高的依赖性,对数据质量和完整性比较敏感。 |
拟合能力 | 其卓越的非线性拟合能力,能够建立和模拟数据中复杂的非线性关系,提高预测的准确度。 | 在数据量有限或模型过于复杂时,深度学习模型可能会过度拟合训练数据,从而降低其在新数据上的泛化能力。 |
自动特征 学习 | 能够自动从原始数据中提取有用的特征,减少了对传统特征提取方法的依赖,提高了效率。 | 无 |
可解释性 | 无 | 决策过程的不透明性,使得其预测难以被直观理解和验证,从而可能限制其在关键领域(如灾害预警)的应用。 |
表2 深度学习模型预测IOD的优缺点分析
Tab.2 Analysis of the advantages and limitations of deep learning models in predicting IOD
项目 | 优势 | 局限性 |
---|---|---|
数据处理 | 能够有效地处理和分析大规模的气候数据集,从历史数据中发现不易被传统方法察觉的复杂模式和关联。 | 需要大量计算资源的同时还对大数据集有较高的依赖性,对数据质量和完整性比较敏感。 |
拟合能力 | 其卓越的非线性拟合能力,能够建立和模拟数据中复杂的非线性关系,提高预测的准确度。 | 在数据量有限或模型过于复杂时,深度学习模型可能会过度拟合训练数据,从而降低其在新数据上的泛化能力。 |
自动特征 学习 | 能够自动从原始数据中提取有用的特征,减少了对传统特征提取方法的依赖,提高了效率。 | 无 |
可解释性 | 无 | 决策过程的不透明性,使得其预测难以被直观理解和验证,从而可能限制其在关键领域(如灾害预警)的应用。 |
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