面向地震应急的自媒体信息挖掘模型  

RESEARCH ON SELF-MEDIA INFORMATION MINING MODEL FOR EARTHQUAKE EMERGENCY RESPONSE

作　　者：苏晓慧[1] 邹再超 苏伟 李林[2] 刘峻明 张晓东 SU Xiao-hui;ZOU Zai-chao;SU Wei;LI Lin;LIU Jun-ming;ZHANG Xiao-dong(School of Information Science and Technology,Beijing Forestry University,Beijing 100083,China;College of Land Science and Technology,China Agricultural University,Beijing 100083,China;Key Laboratory of Remote Sensing for Agri-Hazards,China Agricultural University,Beijing 100083,China)

机构地区：[1]北京林业大学,信息学院,北京100083 [2]中国农业大学,土地科学与技术学院,北京100083 [3]中国农业大学,农业灾害遥感重点实验室,北京100083

出　　处：《地震地质》2019年第3期759-773,共15页Seismology and Geology

基　　金：“十三五”国家重点研发计划项目“天空地协同遥感监测精准应急服务体系构建与示范”(2016YFB0502500)资助

摘　　要：从近几年发生的特大自然灾害事件中可以发现,社交媒体平台正日益成为普通公众及时发布和获取灾情信息的最主要、最便捷的新途径,在这类平台获取的数据中隐藏了大量记录灾情现状的文字、图片等信息。文中首先对海量的历史灾情数据进行统计分析,构建了面向地震应急的信息类别体系和危急度评价体系;基于此训练了用于信息分类的朴素贝叶斯模型,模型的准确率为73.6%;同时采用机器学习模型和语义计算模型这种特征融合的分类方法,对灾情信息的危急度进行评价,评价模型的准确率为89.2%。该模型能够在震后实时地对自媒体中出现的灾情信息进行爬取、分类和评价等操作,可从海量的自媒体信息中挖掘出少量危急又重要的信息,以辅助震后的灾情研判和精准救援。文中最后以2017年8月8日九寨沟地震事件为例,从地震烈度速报、震后精准救援2个角度对挖掘数据的可用性进行了研究分析。From the events of catastrophic natural disasters that have occurred in recent years,it can be found that social media platforms are increasingly becoming the most important and most convenient way for the general public to timely release and obtain information on disasters.The information obtained from such platforms contains a large amount of information in the form of texts,pictures,etc.that record the current situation of the disaster.And it also has characteristics of high efficiency and high spatial distribution to serve the rapid emergency after the earthquake.In this paper,we firstly make a statistical analysis of 32689 pieces of historical disaster data acquired from 5 earthquakes with obvious characteristics,such as post-earthquake disaster events,user s expression habits and so on,and adopts cross-validation method.Then information classification system which includes seven first-level categories and more than 50 second-level categories is constructed.The information classification system and evaluation system of crisis degree for post-earthquake emergency response are constructed both using cross-validation method.The former is referred to the thought of existing classification basis and the experience knowledge of several emergency experts.Based on the five indicators of subject word,action word,degree word,time and position measurement,an evaluation system of critically with four levels of severity,moderate intensity,mildness and others was constructed.Considering the sparse features of self-media information and the large difference in the number of training sets,a naive Bayes model for information classification is trained based on the classification system and evaluation system.Its accuracy rate is 73.6%.At the same time,the classification method of feature fusion of machine learning model and semantic calculation model is used to evaluate the criticality of the disaster information.The accuracy rate of the evaluation model is 89.2%,higher than 85.2% of the semantic computing model and 77% of the

关 键 词：地震应急 自媒体 语义分析 危急度 

分 类 号：P315.9[天文地球—地震学]