基于EEMD-ELM 的航班运行风险混沌预测

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (3): 198-205.

基于EEMD-ELM 的航班运行风险混沌预测

王岩韬^*，李景良，谷润平

中国民航大学空中交通管理学院，天津 300300

收稿日期:2019-12-13 修回日期:2020-02-05 出版日期:2020-06-25 发布日期:2020-06-28
作者简介:王岩韬(1982-)，男，吉林磐石人，副教授.
基金资助:
国家自然科学基金/National Natural Science Foundation of China(U1933103, U1833103)；国家重点研发计划/ National Key Research and Development Program of China(2016YFB0502400).

Chaotic Prediction of Flight Operation Risk Based on EEMD-ELM

WANG Yan-tao, LI Jing-liang, GU Run-ping

School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

Received:2019-12-13 Revised:2020-02-05 Online:2020-06-25 Published:2020-06-28

摘要/Abstract

摘要：

针对航班运行风险可靠预测方案，以某航空公司2016—2018 年航班运行风险数据为基准，通过相空间重构，序列混沌特征的识别，构建基于极端学习机(ELM)的航班运行风险混沌短期预测模型，基于集成经验模态分解(EEMD)阈值降噪方法进行改进；最后，计算风险预测结果，分析不同方式下的预测精度. 结果表明：航班运行风险时间序列具有混沌特征， EEMD方法可抑制序列本征模态函数(IMF)的模态混叠现象；经由EEMD阈值降噪处理后，短期预测结果的修正平均绝对百分误差(MAPE)值显著下降. 证实本文航班运行风险预测方案可行且有效.

关键词: 航空运输, 风险预测, 混沌时间序列, 航班运行风险, 极端学习机, 经验模态分解

Abstract:

To developa reliable prediction methodon flight operation risk, according to the flight operation risk data of a certain airline in 2016-2018, through the reconstruction of phase space reconstruction for the time series, and the identification of series' chaos characteristics, an Extreme Learning Machine (ELM) based chaotic shortterm prediction model for flight operation risk was constructed combining with an iterative prediction method. The model then is improved by Ensemble Empirical Mode Decomposition (EEMD) threshold de-noising method. Finally, the risk prediction results are calculated, and the prediction accuracy of different methods is compared. The results show that the series has chaos characteristics. EEMD method can suppress modal aliasing of the IMF sequences. After EEMD threshold de- noising, the revised MAPE for the short forecast results is significant reduced. It is confirmed that the operation risk prediction method in the paper is applicable and effective.

Key words: air transportation, risk prediction, Chaotic time series, flight operation risk, extreme learning machine, empirical mode decomposition

中图分类号:

V355.2

王岩韬，李景良，谷润平. 基于EEMD-ELM 的航班运行风险混沌预测[J]. 交通运输系统工程与信息, 2020, 20(3): 198-205.

WANG Yan-tao, LI Jing-liang, GU Run-ping. Chaotic Prediction of Flight Operation Risk Based on EEMD-ELM[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(3): 198-205.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract20066.shtml

http://www.tseit.org.cn/CN/Y2020/V20/I3/198

参考文献

[1] BOWEN B D, HEADLEY D E, LUEDTKE J R. Airline quality rating[J]. NIAR Report, 1991, 91(11): 1-10.

[2] LOWER M, MAGOTT J, SKORUPSKI J. Analysis of air traffic incidents using event trees with fuzzy probabilities [J]. Fuzzy Sets & Systems, 2016, 293(C): 50-79.

[3] KOKANGUL A, POLAT U, DAGSUYU C. A new approximation for risk assessment using the AHP and Fine Kinney methodologies[J]. Safety Science, 2017(91): 24-32.

[4] FAA. Flight risk assessment tool[EB/OL]. (2007-08-02) [2018-08-31]. http: //www. faa. gov/other_visit/aviation _industry/airline_operators/airline_safety/info/all_infos/ media/2007/info07015.pdf.

[5] NATA.RA Check[EB/OL].(2012-01-23)[2018-08-31]. http://www.nataiccheck.aero/racheck/.

[6] 孙瑞山, 刘汉辉. 航空公司安全评估理论与实践[J]. 中国安全科学学报, 1999, 9(3): 72-76, 81. [SUN R S, LIU H H. Assessment theory and practice for airline safety[J]. China Safety Science Journal, 1999, 9(3): 72-76, 81.]

[7] 王岩韬, 李蕊, 卢飞, 等. 基于多因素分析的航班运行风险评估体系[J]. 天津工业大学学报, 2014, 33(3): 84- 88. [WANG Y T, LI R, LU F, et al. A risk assessment system of flight operation based on multiple factor analysis[J]. Journal of Tianjin Polytechnic University, 2014, 33(3): 84–88.]

[8] 王岩韬, 赵嶷飞. 基于多算法协作的航班运行风险辨识研究[J]. 中国安全科学学报, 2018, 28(6): 166-172. [WANG Y T, ZHAO Y F. Research on flight operations risk identification based on multi-algorithm collaboration[J]. China Safety Science Journal, 2018, 28 (6): 166-172.]

[9] COURTENEY H. Risk management choices-Where to invest?[C]. Honolulu, HI, United states: Flight Safety Foundation Incorporated, 2008: 297-335.

[10] PROSPEROFP7. Aviation sector collaborates to make ultra-safe travel even safer[EB/OL]. (2015-11-12) [2018.10.26]. http://www.prosperofp7.eu/news.

[11] LALIS A, SOCHA V, KRAUS J, et al. Conditional and unconditional safety performance forecasts for aviation predictive risk management: Piscataway, NJ, USA, 3-10 March 2018[C]. Big Sky, MT, USA: IEEE, 2018.

[12] ZHANG X, MAHADEVANS. Ensemble machine learning models for aviation incident risk prediction[J]. Decision Support Systems, 2019(116): 48-63.

[13] 王岩韬, 刘宏, 唐建勋, 等. 动态预测技术在航班运行风险中的应用[J]. 控制与决策, 2019, 34(9): 1946- 1954. [WANG Y T, LIU H, TANG J X, et al. Dynamic prediction technology in the application of flight operation risk[J]. Control and Decision, 2019, 34(9): 1946-1954. ]

[14] 沈力华, 陈吉红, 曾志刚, 等. 基于鲁棒极端学习机的混沌时间序列建模预测[J]. 物理学报, 2018, 67(3): 32- 42. [SHEN L H, CHEN J H, ZENG Z G, et al. Chaotic time series prediction based on robust extreme learning machine[J]. Acta Physica Sinica, 2018, 67(3): 32-42. ]

[15] DONOHO D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.

[16] 陈益, 李书. 改进的小波阈值消噪法应用于超声信号处理[J]. 北京航空航天大学学报, 2006(4): 466-470. [CHEN Y, LI S. Application of improved threshold denoising based on wavelet transform to ultrasonic signal processing[J]. Journal of Beijing University of Aeronautics and Astronautics, 2006(4): 466-470. ]

[17] 牛阔, 张朝霞, 王娟芬, 等. 改进EMD与小波阈值相结合的光生混沌信号降噪[J]. 现代电子技术, 2018, 41 (17): 53-58. [NIU K, ZHANG Z X, WANG J F, et al. Photogenerated chaos signal noise reduction based on improved EMD and wavelet threshold[J]. Modern Electronics Technique, 2018, 41(17): 53-58. ]