|本期目录/Table of Contents|

[1]邱凌峰,韩昕格,胡啸峰.基于机器学习的恐怖袭击事件后果预测方法研究[J].中国安全生产科学技术,2020,16(1):175-181.[doi:10.11731/j.issn.1673-193x.2020.01.029]
　QIU Lingfeng,HAN Xinge,HU Xiaofeng.Study on method of consequence prediction for terrorist attacks based on machine learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2020,16(1):175-181.[doi:10.11731/j.issn.1673-193x.2020.01.029]
点击复制

基于机器学习的恐怖袭击事件后果预测方法研究
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
16
期数:
2020年1期
页码:
175-181
栏目:
特邀专栏
出版日期:
2020-01-30

文章信息/Info

Title:
Study on method of consequence prediction for terrorist attacks based on machine learning
文章编号:
1673-193X(2020)-01-0175-07
作者:
邱凌峰; 韩昕格; 胡啸峰
(1.安全防范技术与风险评估公安部重点实验室，北京 102623；
2.云从科技 中台产品中心,上海 200120)
Author(s):
QIU Lingfeng;  HAN Xinge;  HU Xiaofeng
(1.Key Laboratory of Security Technology & Risk Assessment,Ministry of Public Security,Beijing 102623,China;
2.MiddleOffice Product Center,CloudWalk,Shanghai 200120,China)
关键词:
恐怖袭击; 机器学习; 后果预测; 随机森林; 岭回归
Keywords:
terrorist attacks;  machine learning;  consequence prediction;  random forest;  ridge regression
分类号:
X913.4
DOI:
10.11731/j.issn.1673-193x.2020.01.029
文献标志码:
A
摘要:
恐怖袭击事件通常会造成严重的人员伤亡、财产损失和社会影响，针对在不同场景下发生恐怖袭击所造成的后果进行预测是目前应对恐怖袭击事件急需解决的问题之一。利用多源数据，首先基于随机森林算法对恐怖袭击事件是否造成死伤进行分类预测，进而基于岭回归算法预测事件造成的具体死伤人数。研究结果表明：随机森林在测试集上对有死伤事件的召回率达到0.85，岭回归预测死亡和受伤人数的平均绝对误差分别小于1人和2人。研究结果可为反恐资源配置优化、预防恐怖袭击事件和减少其造成的损害提供辅助决策支持。
Abstract:
The terrorist attacks usually cause serious casualties,property loss and social impact,so the prediction of consequence caused by the terrorist attacks under various scenarios is one of the problems which urgently need to be solved for the response of terrorist attacks at present.Through using the multisource data,the classified prediction on whether the terrorist attacks would cause casualties was carried out based on the random forest algorithm,and the specific casualties caused by the terrorist attacks were predicted by using the ridge regression algorithm.The results showed that the recall rate for the casualties events of random forest on the testing sets reached 0.85,and the mean absolute error of the ridge regression prediction on the death toll and the number of injury was less than 1 and 2 respectively.The results can provide decisionmaking support for optimizing the allocation of counterterrorism resources,as well as preventing and reducing the terrorist attacks and the damage caused by them.

参考文献/References:

［1］中央政府门户网站.国务院发布《国家突发公共事件总体应急预案》［EB/OL］.(2006-01-08)［2020-01-20］.http://www.gov.cn/jrzg/2006-01/08/content_150878.htm.
［2］ANIL A,KUMAR D,SHARMA S,et al.Link Prediction Using Social Network Analysis over Heterogeneous Terrorist Network［C］// 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity).IEEE,2015.
［3］傅子洋,徐荣贞,刘文强.基于贝叶斯网络的恐怖袭击预警模型研究［J］.灾害学,2016,31(3):184-189. FU Ziyang,XU Rongzhen,LIU Wenqiang.Research on terrorist attack warning model based on bayesian network［J］.Journal of Catastrophology,2016,31(3):184-189.
［4］唐正,朱衍丞,邱凌峰,等.基于特征画像的恐怖组织袭击偏好研究［J］.软件导刊,2019,18(1):128-131. TANG Zheng,ZHU Yancheng,QIU Lingfeng,et al.Research on attacking preference of terrorist organization based on feature profiling ［J］.Software Guide,2019,18(1):128-131.
［5］项寅.基于改进神经网络的恐怖袭击风险预警系统［J］.灾害学,2018,33(1):183-189. XIANG Yin.Warning system of terrorist attacks based on improved neural network［J］.Journal of Catastrophology,2018,33(1):183-189.
［6］KENGPOL A,NEUNGRIT P.A decision support methodology with risk assessment on prediction of terrorism insurgency distribution range radius and elapsing time:an empirical case study in Thailand［J］.Computers & Industrial Engineering,2014,75(1):55-67.
［7］BAIG A R,JABEEN H.Big data analytics for behavior monitoring of students［J］.Procedia Computer Science,2016,82:43-48.
［8］SACHAN A,ROY D.TGPM:terrorist group prediction model for counter terrorism［J］.International Journal of Computer Applications,2012,44(10):49-52.
［9］邱凌峰,胡啸峰,顾海硕,等.基于机器学习和脆弱国家指数的全球恐怖袭击预测研究［J］.灾害学,2019,34(2):211-214. QIU Lingfeng,HU Xiaofeng,GU Haishuo,et al.Study on prediction of global terrorist attacks based on machine learning and fragile states index［J］.Journal of Catastrophology,2019,34(2):211-214.
［10］华雅伦,王奇.基于GTD数据库的欧洲反恐形势分析及对我国的启示［J］.犯罪研究,2018(5):91-105. HUA Yalun,WANG Qi.An analysis of the anti-terrorism situation in Europe and its enlightenment to China based on GTD［J］.Chinese Criminology Review,2018(5):91-105.
［11］周秋君.恐怖主义在欧洲发展的新态势及其原因分析［J］.社会科学,2019(2):29-37. ZHOU Qiujun.The new situation and causes of terrorism in Europe［J］.Journal of Social Sciences,2019(2):29-37.
［12］李益斌.欧洲恐怖主义的新态势及原因分析——基于聚类分析法［J］.情报杂志,2018,37(3):55-63. LI Yibin.New trends and causes of terrorism in Europe:based on clustering analysis［J］.Journal of Intelligence,2018,37(3):55-63.
［13］GRAY K R,ALJABAR P,HECKEMANN R A,et al.Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease［J］.Neuroimage,2013,65:167-175.
［14］MCDONALD G C.Ridge regression［J］.Wiley Interdisciplinary Reviews Computational Statistics,2010,1(1):93-100.
［15］牟瑞芳,杨锐,王列妮.熟悉环境条件下的公共场所人员疏散仿真模型研究［J］.中国安全生产科学技术,2015,11(5):181-186. MOU Ruifang,YANG Rui,WANG Lieni.Study on simulation model of public place evacuation in a familiar environment［J］.Journal of Safety Science and Technology,2015,11(5):181-186.
［16］吕伟,穆治国,刘丹.大型购物中心人员疏散引导模拟优化研究［J］.中国安全生产科学技术,2019,15(5):136-141. LYU Wei,MU Zhiguo,LIU Dan.Study on optimal simulation of pedestrian evacuation guidance in large shopping mall［J］.Journal of Safety Science and Technology,2019,15(5):136-141.

相似文献/References:

[1]龙圣杰,刘衍民,隋杰.生化恐怖袭击情境下引导疏散中行人行为仿真研究[J].中国安全生产科学技术,2016,12(7):174.[doi:10.11731/j.issn.1673-193x.2016.07.031]
　LONG Shengjie,LIU Yanmin,SUI Jie.Study on simulation of pedestrian behaviors in guided evacuation under biochemical terrorist attack[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2016,12(1):174.[doi:10.11731/j.issn.1673-193x.2016.07.031]
[2]张雪蕾,汪明,曹寅雪,等.3种典型机器学习方法在灾害敏感性评估中的对比[J].中国安全生产科学技术,2018,14(7):79.[doi:10.11731/j.issn.1673-193x.2018.07.012]
　ZHANG Xuelei,WANG Ming,CAO Yinxue,et al.Comparison of three typical machine learning methods in susceptibility assess-ment of disasters[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(1):79.[doi:10.11731/j.issn.1673-193x.2018.07.012]
[3]邱凌峰,胡啸峰,周睿,等.基于机器学习的典型社会安全事件发生规律研究及对雄安新区的启示[J].中国安全生产科学技术,2018,14(10):11.[doi:10.11731/j.issn.1673-193x.2018.10.002]
　QIU Lingfeng,HU Xiaofeng,ZHOU Rui,et al.Research on the occurrence regularity of typical social security incidents based on machine learning and implications for Xiongan New Area[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(1):11.[doi:10.11731/j.issn.1673-193x.2018.10.002]
[4]杨莉琼,蔡利强,古松.基于机器学习方法的安全帽佩戴行为检测[J].中国安全生产科学技术,2019,15(10):152.[doi:10.11731/j.issn.1673-193x.2019.10.024]
　YANG Liqiong,CAI Liqiang,GU Song.Detection on wearing behavior of safety helmet based on machine learning method[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(1):152.[doi:10.11731/j.issn.1673-193x.2019.10.024]
[5]卢子涵,胡啸峰,邱凌峰.基于机器学习的侵财类案件危害程度分析[J].中国安全生产科学技术,2019,15(12):29.[doi:10.11731/j.issn.1673-193x.2019.12.005]
　LU Zihan,HU Xiaofeng,QIU Lingfeng.Analysis on hazard degree of cases of encroaching on property based on machine learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(1):29.[doi:10.11731/j.issn.1673-193x.2019.12.005]
[6]王媛媛,诸立超,潘金山.地铁恐怖袭击下乘客疏散交通方式调查研究[J].中国安全生产科学技术,2020,16(2):149.[doi:10.11731/j.issn.1673-193x.2020.02.024]
　WANG Yuanyuan,ZHU Lichao,PAN Jinshan.Investigation and study on evacuation traffic mode of passengers in metro under terrorist attack[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2020,16(1):149.[doi:10.11731/j.issn.1673-193x.2020.02.024]
[7]辛悦,曾昭龙,沈兵,等.基于多主体仿真的重点城区脏弹袭击风险分析*[J].中国安全生产科学技术,2021,17(3):33.[doi:10.11731/j.issn.1673-193x.2021.03.005]
　XIN Yue,ZENG Zhaolong,SHEN Bing,et al.Risk analysis on dirty bomb attack in key urban areas based on multi-agent simulation[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(1):33.[doi:10.11731/j.issn.1673-193x.2021.03.005]
[8]徐鲁帅,凌晓,马娟娟,等.基于DE-BPNN模型的含腐蚀缺陷管道失效压力预测*[J].中国安全生产科学技术,2021,17(3):91.[doi:10.11731/j.issn.1673-193x.2021.03.014]
　XU Lushuai,LING Xiao,MA Juanjuan,et al.Prediction on failure pressure of pipeline containing corrosion defects based on DE-BPNN[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(1):91.[doi:10.11731/j.issn.1673-193x.2021.03.014]
[9]孙宝财,朱蔡文,凌晓.基于SVR的含缺陷管道剩余强度研究*[J].中国安全生产科学技术,2022,18(2):172.[doi:10.11731/j.issn.1673-193x.2022.02.026]
　SUN Baocai,ZHU Caiwen,LING Xiao.Research on residual strength of pipeline with defects based on SVR[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(1):172.[doi:10.11731/j.issn.1673-193x.2022.02.026]
[10]段在鹏,李帆,邱少辉,等.地铁沿线老旧房屋结构安全预警模型*[J].中国安全生产科学技术,2022,18(3):162.[doi:10.11731/j.issn.1673-193x.2022.03.025]
　DUAN Zaipeng,LI Fan,QIU Shaohui,et al.Early warning model for structural safety of old buildings along metro lines[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(1):162.[doi:10.11731/j.issn.1673-193x.2022.03.025]

备注/Memo

备注/Memo:
收稿日期： 2020-01-16
* 基金项目： 国家自然科学基金项目( 71704183)
作者简介： 邱凌峰，硕士研究生，主要研究方向为安防大数据、社会安全风险评估。
通信作者： 胡啸峰，博士，副教授，主要研究方向为风险评估。
更新日期/Last Update: 2020-03-02