[1]金龙,黄颖,姚才,等.人工智能技术的热带气旋预报综述（之二）——流形学习、智能计算及深度学习的热带气旋预报方法[J].气象研究与应用,2020,41(04):5-12.[doi:10.19849/j.cnki.CN45-1356/P.2020.4.02]
　Jin Long,Huang Ying,Yao Cai,et al.Summary of tropical cyclone forecasting based on artificial intelligence technology (part 2)——tropical cyclone forecasting methods based on manifold learning, intelligent calculation and deep learning[J].Journal of Meteorological Research and Application,2020,41(04):5-12.[doi:10.19849/j.cnki.CN45-1356/P.2020.4.02]
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人工智能技术的热带气旋预报综述（之二）——流形学习、智能计算及深度学习的热带气旋预报方法()
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气象研究与应用[ISSN:1673-8411/CN:45-1356/P]

卷:
第41卷
期数:
2020年04期
页码:
5-12
栏目:
广西气象学会成立60周年纪念专刊
出版日期:
2020-12-31

文章信息/Info

Title:
Summary of tropical cyclone forecasting based on artificial intelligence technology (part 2)——tropical cyclone forecasting methods based on manifold learning, intelligent calculation and deep learning
作者:
金龙1;  黄颖2;  姚才1;  黄小燕2;  赵华生2
1. 广西壮族自治区气候中心, 南宁 530022;
2. 广西壮族自治区气象科学研究所, 南宁 530022
Author(s):
Jin Long1;  Huang Ying2;  Yao Cai1;  Huang Xiaoyan2;  Zhao Huasheng2
1. Guangxi Climate Center, Nanning Guangxi 530022;
2. Guangxi Institute of Meteorological Sciences, Nanning Guangxi 530022
关键词:
热带气旋; 智能计算; 流形学习; 数据挖掘; 深度学习
Keywords:
tropical cyclone; intelligent computing; manifold learning; data mining; deep learning
分类号:
P456
DOI:
10.19849/j.cnki.CN45-1356/P.2020.4.02
摘要:
继"人工智能技术的热带气旋预报综述（之一）"有关BP神经网络和集成方法的热带气旋预报研究和业务应用进行详细综述后，本文将进一步综述流形学习方法在热带气旋预报因子数据挖掘中的应用，以及各种智能计算模型，包括粒子群算法、模糊算法、概率算法，及深度学习方法在热带气旋预报中的应用研究成果，并对今后气象领域的人工智能发展进行初步设想探讨，以期能为有效提高人工智能方法的气象灾害预报能力提供有益参考。
Abstract:
Following a detailed review of tropical cyclone forecasting research and operational applications of BP neural network and integrated methods in "Review of Tropical Cyclone Forecast Based on Artificial Intelligence Technology (Part 1)", this article further reviewed the application of manifold learning methods in data mining of tropical cyclone predictors. Besides, the applications of various intelligent computing models, including particle swarm algorithm, fuzzy algorithm, probability algorithm, and deep learning methods in tropical cyclone forecasting were also discussed. A preliminary discussion on the development of artificial intelligence in the field of meteorology in the future is expected to provide a useful reference for effectively improving the ability of artificial intelligence to forecast meteorological disasters.

参考文献/References:

[1] 李程怡. 人工智能的关键技术及相关应用[J]. 科技创新与应用. 2020(12):156-157,160.
[2] 朱玲,吴心玥.人工智能在气象领域的应用述评[J]. 广东气象,2019,41(1):35-39.
[3] 张韧, 蒋国荣,余志豪,等. 利用神经网络计算方法建立太平洋副高活动的预报模型[J]. 应用气象学报,2000,11(4):474-483.
[4] 金龙. 神经网络气象预报建模理论方法与应用[M]. 2004, 北京:气象出版社.
[5] 姚才, 金龙, 黄明策, 等. 遗传算法与神经网络相结合的热带气旋强度预报方法试验[J]. 海洋学报(中文版),2007,29(4):11-19.
[6] 林开平, 刘春霞, 黄颖, 等. 南海热带气旋强度预报的线性模型与非线性模型研究[J]. 热带气象学报,2013,29(6):1001-1007.
[7] 付文博, 孙涛, 梁藉, 等. 深度学习原理及应用综述[J]. 计算机科学,2018,45(S1):11-15,40.
[8] Combinido J S, Mendoza J R, Aborot J. A convolutional neural network approach for estimating tropical cyclone intensity using satellite-based infrared images[C].24th International Conference on Pattern Recognition,2018:1474-1480.
[9] 尹峻松, 肖健, 周宗潭, 等. 非线性流形学习方法的分析与应用[J].自然科学进展, 2007,17(8):1015-1025.
[10] 李鑫, 续婷, 胡红萍, 等.局部流形学习在SAR目标分类中的应用[J]. 现代雷达, 2020, 42(4):33-36,40.
[11] 王云艳, 罗冷坤, 王重阳. 基于流形学习的光学遥感图像分类[J]. 计算机工程与科学,2019,41(7):1212-1219.
[12] 刘开南,冯新扬,邵超.一种面向图像分类的流形学习降维算法[J].计算机应用与软件,2019,36(8):210-213+229.
[13] Tenenbaum J B,Silva V D,Langford J C.A global geometric framework for nonlinear dimensionality reduction[J]. Science,2000, 290(5500):2319-2323.
[14] Roweis S T. Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290(5500):2323-2326.
[15] Torgerson W S. Multidimensional scaling:I theory and method[J]. Psychometrika,1952,17(4):401-419.
[16] Jin L, Huang Y. A particle swarm optimization-neural network prediction model for typhoon intensity based on Isometric Mapping algorithm[C]. Fifth International Joint Conference on Computational Sciences and Optimization, 2012:857-861.
[17] Huang Y, Jin L. A prediction scheme with genetic neural network and Isomap algorithm for tropical cyclone intensity change over Western North Pacific[J]. Meteorology and Atmospheric Physics,2013,121(3-4):143-152.
[18] Lin K P,Chen B L,Dong Y,et al. A genetic neural network ensemble prediction model based on Locally Linear Embedding for typhoon intensity[C]. IEEE 8th Conference on Industrial Electronics and Applications, 2013:137-142.
[19] Jin L, Huang X Y, Shi X M, et al. A fuzzy neural network prediction model based on manifold learning to reduce dimensions for typhoon intensity[C]. IEEE 8th Conference on Industrial Electronics and Applications,2013:562-566.
[20] 黄颖, 金龙, 黄小燕, 等. 基于局部线性嵌人的人工智能台风强度集合预报模型[J]. 气象,2014,40(7):806-815.
[21] 朱东青. 基于PSO-BP神经网络的南海热带气旋预报研究[D]. 南京师范大学. 2012.
[22] Zhao H S, Jin L, Huang Y, et al. An application of ensemble prediction for typhoon intensity based on MDS and PSO-ANN[C].Fifth International Joint Conference on Computational Sciences and Optimization, 2012:885-888.
[23] 黄小燕,史旭明,刘苏东,等. 模糊神经网络方法在热带气旋强度预报中的应用研究[J]. 高原气象,2009,28(6):1408-1413.
[24] Specht D F. Probabilistic neural networks[J]. Neural Networks, 1990, 3(2):109-118.
[25] Huang X Y, Guan Z Y, He L ea al. A PNN prediction scheme for local tropical cyclone intensity over the South China Sea[J]. Natural Hazards, 2016, 81(2):1249-1267.
[26] 朱雷. 基于神经网络委员会机器的南中国海台风路径预报模型研究[D]. 华东师范大学. 2017.
[27] 周笑天. 基于混合模式集合预报的台风路径预报优化方法研究[D]. 浙江大学. 2019.
[28] 周笑天, 张丰, 杜震洪, 等. 基于神经网络集合预报的台风路径预报优化[J]. 浙江大学学报(理学版), 2020, 47(2):196-217.
[29] 黄小燕, 金龙. 条件数在台风移动路径预报中的应用[J].自然灾害学报, 2007, 16(3):35-40.
[30] 农吉夫. 预报因子选择的条件数方法及其在台风强度预报中的应用[J]. 数学的实践与认识, 2014, 44(23):146-152.
[31] Suykens J A K. Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3):393-300.
[32] 王鑫, 杨慧中. 基于LS-SVM的热带气旋强度预报[J]. 江南大学学报(自然科学版), 2010, 9(4):400-403.
[33] 吕庆平, 罗坚, 朱坤等. 基于SVM的气候持续法在热带气旋路径预报中的应用试验[J]. 海洋预报,2009,26(1):76-83.
[34] 顾锦荣,刘华强,刘向陪. 基于遗传算法-支持向量机模型在热带气旋强度预报中的应用[J]. 海洋预报, 2011,28(3):8-14.
[35] 钱燕珍, 孙军波, 陈佩燕, 等. 用数值预报释用方法做近海及登陆热带气旋强度预报[J]. 气象,2013,39(6):710-718.
[36] Wimmers A,Velden C, Cossuth J H. Using deep learning to estimate tropical cyclone intensity from satellite passive microwave imagery[J]. Monthly Weather Review, 2019, 147(6):2261-2282.
[37] Chen B F, Chen B,Lin H T, et al. Elsberry. Estimating tropical cyclone intensity by satellite imagery utilizing convolutional neural networks[J].Weather and Forecasting,2019,34(2):447-465.
[38] Pradhan R, Aygun R S, Maskey M, et al. Tropical cyclone intensity estimation using a deep convolutional neural network[J]. IEEE Transactions on Image Processing, 2018, 27(2):692-702.
[39] Lee J, Im J, Cha D H, et al. Tropical cyclone intensity estimation using multi-dimensional convolutional neural networks from geostationary satellite data[J]. Remote Sensing, 2019, 12(1):108.
[40] Zhang R, Liu Q S, Hang R L. Tropical cyclone intensity estimation using two-branch convolutional neural network from infrared and water vapor images[J].IEEE Transactions on Geoscience and Remote Sensing, 2020,58(1):586-597.
[41] Combinido J S, Mendoza J R, Aborot J. A convolutional neural network approach for estimating tropical cyclone intensity using satellite-based infrared images[C]. 24th International Conference on Pattern Recognition, 2018:1474-1480.
[42] Pan B,Xu X,Shi Z W.Tropical cyclone intensity prediction based on recurrent neural networks[J].Electronics Letters, 2019,55(7):413-415.
[43] Sharma A K, Prasad V, Kumar R, et al. Analysis on the occurrence of tropical cyclone in the South Pacific Region using recurrent neural network with LSTM[C].International Conference on Neural Information Processing,2018:476-486.
[44] Chandra R, Dayal K S. Coevolutionary recurrent neural networks for prediction of rapid intensification in wind intensity of tropical cyclones in the South Pacific Region[C].International Conference on Neural Information Processing,2015:43-52.

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备注/Memo

备注/Memo:
收稿日期:2020-10-09。
基金项目:广西自然科学基金（2018GXNSFAA281229，2017GXNSFDA198030，2018GXNSFAA294128）、国家自然科学基金（42065004，41765002）
作者简介:金龙(1952-),男,研究员,主要从事人工智能技术方法研究与业务应用工作。E-mail:jinlong01@163.com
通讯作者:黄颖(1983-),女,高级工程师,主要从事天气预报技术方法研究与业务应用工作。E-mail:yinger2001@126.com
更新日期/Last Update: 1900-01-01