|本期目录/Table of Contents|

[1]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):79-85.[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(7):79-85.[doi:10.11731/j.issn.1673-193x.2018.07.012]
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3种典型机器学习方法在灾害敏感性评估中的对比
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
14
期数:
2018年7期
页码:
79-85
栏目:
职业安全卫生管理与技术
出版日期:
2018-07-31

文章信息/Info

Title:
Comparison of three typical machine learning methods in susceptibility assess-ment of disasters
文章编号:
1673-193X(2018)-07-0079-07
作者:
张雪蕾12汪明12曹寅雪12刘凯12洪超裕12
(1.环境演变与自然灾害教育部重点实验室,北京 100875;2.北京师范大学 地理科学学部减灾与应急管理研究院,北京 100875)
Author(s):
ZHANG Xuelei12 WANG Ming12 CAO Yinxue12 LIU Kai12 HONG Chaoyu12
(1. Key Laboratory of Environmental Change and Natural Disaster, MOE, BNU, Beijing 100875, China;2. Academy of Disaster Reduction and Emergency Management, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)
关键词:
机器学习敏感性评估K近邻朴素贝叶斯随机森林
Keywords:
machine learning susceptibility assessment K-Nearest Neighbor Nave Bayes Random Forest
分类号:
X913.4
DOI:
10.11731/j.issn.1673-193x.2018.07.012
文献标志码:
A
摘要:
为了比较不同机器学习方法在灾害敏感性评估中的适用性,将K近邻、朴素贝叶斯和随机森林3种典型机器学习方法运用到灾害敏感性评估中,比较3种方法在预测准确率、混淆矩阵和敏感性图上的异同;并基于深圳市电动自行车出险数据以及孕灾环境数据,运用3种方法评估深圳市电动自行车出险的敏感性,进而绘制敏感性图,探讨3种方法对于不同性质数据的适用条件,为开展灾害敏感性评估工作时选取更为适宜的方法,提供参考建议。
Abstract:
In order to compare the applicability of different machine learning methods in the susceptibility assess-ment of disasters, three typical machine learning methods including K-Nearest Neighbor, Nave Bayes and Random Forest were applied in the susceptibility assessment of disasters, and the similarities and differences of three methods in the prediction accuracy, confusion matrix and susceptibility map were compared. Based on the accidents data and disaster-pregnant environment data of electric bicycles in Shenzhen, the susceptibility of electric bicycle accidents in Shenzhen were assessed by using three methods, then the susceptibility maps were drawn, and the applicable conditions of three methods for the data with different properties were discussed. The results provide the reference suggestions for selecting the more appropriate method when conducting the susceptibility assessment of disasters.

参考文献/References:

[1]SONG Y, GONG J, GAO S, et al. Susceptibility assessment of earthquake-induced landslides using bayesian network: a case study in Beichuan, China[J]. Computers & Geosciences, 2012, 42(5):189-199.
[2]DAS I,STEIN A,KERLE N, et al. Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models[J]. Geomorphology, 2012, 179(60):116-125.
[3]NEFESLIOGLU H A, GOKCEOGLU C, SONMEZ H. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps[J]. Engineering Geology, 2008, 97(3):171-191.
[4]Gómez H, Kavzoglu T. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela[J]. Engineering Geology, 2005, 78(1-2):11-27.
[5]ERMINI L, CATANI F, CASAGLI N. Artificial neural networks applied to landslide susceptibility assessment[J]. Geomorphology, 2005, 66(1):327-343.
[6]YAO X, THAM L G, DAI F C. Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China[J]. Geomorphology, 2008, 101(4):572-582.
[7]MICHELETTI N,KANEVSKI M,BAI S,et al.Intelligent analysis of landslide data using machine learning algorithms[M].Berlin Heidelberg:Springer,2013:161-167.
[8]桑应宾. 基于K近邻的分类算法研究[D]. 重庆:重庆大学, 2009.
[9]BREIMAN L. Bagging predictors[J]. Machine Learning, 1996, 24(2):123-140.
[10]贺鸣, 孙建军, 成颖. 基于朴素贝叶斯的文本分类研究综述[J]. 情报科学, 2016, 34(7):147-154.HE Ming, SUN Jianjun,CHENG Ying.Text classification based on naive bayes:a review[J]. Information Science, 2016,34(7):147-154.
[11]阿曼.朴素贝叶斯分类算法的研究与应用[D].大连:大连理工大学,2014.
[12]BREIMAN L. Random Forests[J]. Machine Learning, 2001, 45(1):5-32.
[13]HO T K. The random subspace method for constructing decision forests[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1998, 20(8):832-844.
[14]冯少荣. 决策树算法的研究与改进[J]. 厦门大学学报(自然版), 2007, 46(4):496-500.FENG Shaorong.Research and improvement of decision trees algorithm[J]. Journal of Xiamen University (Natural Science), 2007,46(4):496-500.
[15]伊卫国. 基于关联规则与决策树的预测方法研究及其应用[D]. 大连:大连海事大学, 2012.
[16]张宇.决策树分类及剪枝算法研究[D].哈尔滨:哈尔滨理工大学, 2009.
[17]王黎明. 决策树学习及其剪枝算法研究[D]. 武汉:武汉理工大学, 2007.
[18]丁世飞, 齐丙娟, 谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报, 2011, 40(1):2-10.DING Shifei, QI Bingjuan,TAN Hongyan.An overview on theory and algorithm of support vector machines[J]. Journal of University of Electronic Science and Technology of China,2011,40(1):2-10.
[19]梁礼明, 钟震, 陈召阳. 支持向量机核函数选择研究与仿真[J]. 计算机工程与科学, 2015, 37(6):1135-1141.LIANG Liming, ZHONG Zhen, CHEN Zhaoyang.Research and simulation of kernel function selection for support vector machine[J]. Computer Engineering & Science, 2015,37(6):1135-1141.
[20]周志华.机器学习[M].北京:清华大学出版社, 2016.
[21]刘敏, 郎荣玲, 曹永斌. 随机森林中树的数量[J]. 计算机工程与应用, 2015, 51(5):126-131.LIU Min, LANG Rongling,CAO Yongbin.Number of trees in random forest[J]. Computer Engineering and Applications. 2015, 51(5):126-131.
[22]曹正凤.随机森林算法优化研究[D].北京:首都经济贸易大学, 2014.
[23]马骊.随机森林算法的优化改进研究[D].广州:暨南大学, 2016.
[24]李荣华. 深圳电动自行车暴增至400万辆[N]. 南方日报, 2015-07-21.
[25]中国青年网. 深圳交警局召开“禁摩限电”记者会:整治并非随意之举[EB/OL].(2016-04-05)[2018-07-11]. http://news.youth.cn/gn/201604/t20160405_7821191.htm.
[26]CAO Y, WANG M, LIU K. Wildfire Susceptibility Assessment in Southern China:A Comparison of Multiple Methods[J]. International Journal of Disaster Risk Science, 2017, 8(2):1-18.
[27]孔英会, 景美丽. 基于混淆矩阵和集成学习的分类方法研究[J]. 计算机工程与科学, 2012, 34(6):111-117.KONG Yinghui, JING Meili.Research of the classification method based on confusion matrixes and ensemble learning[J]. Omputer Engineering & Science, 2012,34(6): 111-117.
[28]毕凯, 王晓丹, 姚旭,等. 一种基于Bagging和混淆矩阵的自适应选择性集成[J]. 电子学报, 2014, 42(4):711-716.BI Kai,WANG Xiaodan,YAO Xu,et al.Adaptively selective ensemble algorithm based on bagging and confusion matrix[J]. Acta Electronica Sinica, 2014,42(4): 711-716.

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备注/Memo

备注/Memo:
国家自然科学基金项目(41671503)
更新日期/Last Update: 2018-08-09