|本期目录/Table of Contents|

[1]孙瑞山,李重锋.基于随机森林的飞机长着陆预警方法[J].中国安全生产科学技术,2021,17(5):182-186.[doi:10.11731/j.issn.1673-193x.2021.05.028]
 SUN Ruishan,LI Chongfeng.Early-warning method of aircraft long landing based on random forest[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(5):182-186.[doi:10.11731/j.issn.1673-193x.2021.05.028]
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基于随机森林的飞机长着陆预警方法
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
17
期数:
2021年5期
页码:
182-186
栏目:
职业安全卫生管理与技术
出版日期:
2021-05-31

文章信息/Info

Title:
Early-warning method of aircraft long landing based on random forest
文章编号:
1673-193X(2021)-05-0182-05
作者:
孙瑞山李重锋
(1.中国民航大学 民航安全科学研究所,天津 300300;
2.中国民航大学 经济与管理学院,天津 300300)
Author(s):
SUN Ruishan LI Chongfeng
(1.Research Institute of Civil Aviation Safety,Civil Aviation University of China,Tianjin 300300,China;
2.Economics and Management College,Civil Aviation University of China,Tianjin 300300,China)
关键词:
飞行安全飞行数据长着陆随机森林预警方法
Keywords:
flight safety flight data long landing random forest early-warning method
分类号:
X949
DOI:
10.11731/j.issn.1673-193x.2021.05.028
文献标志码:
A
摘要:
为降低飞机冲出跑道事故造成的人员伤亡和财产损失,以长着陆事件为研究对象,探讨1种有效的长着陆事件预警方法。提出应用随机森林模型对长着陆事件进行预警,给出模型的特征筛选、构建及评价方法,并使用实际飞行数据验证模型有效性。结果表明:基于随机森林的飞机长着陆预警模型精确率为88.41%,召回率为87.14%,模型可以有效对长着陆事件进行预警。
Abstract:
In order to reduce the casualties and property losses caused by the runway overrun accident of aircraft,taking the long landing events as the research object,an effective early-warning method of long landing events was discussed.It was put forward to apply the random forest model to carry out the early-warning of long landing events,then the feature selection,construction and evaluation methods of the model were given.The effectiveness of the early-warning method of long landing events based on the random forest model was verified by the actual flight data.The results showed that the accuracy rate of the model was 87.14%,and the recall rate was 88.41%.The model can effectively warn the long landing events.

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备注/Memo

备注/Memo:
收稿日期: 2021-01-11
作者简介: 孙瑞山,博士,教授,主要研究方向为民航安全管理、航空人因工程、飞行原理和飞机飞行性能。
通信作者: 李重锋,硕士研究生,主要研究方向为飞机运行安全、飞行数据挖掘。
更新日期/Last Update: 2021-06-03