基于社交媒体的暴雨灾情信息实时挖掘与分析——以2019年“4·11深圳暴雨”为例
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X43

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国家自然科学基金重大研究计划重点项目(91846203);国家自然科学基金青年项目(71601070)


Mining and analysis of rainstorm disaster information based on social media—Case study of Shenzhen rainstorm on April 11, 2019
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    摘要:

    在暴雨天气过程中,实时获取详细的暴雨灾情信息,尤其是灾害带来的影响和后果,对灾情的及时响应和评估有着重要的意义。结合隐含狄利克雷分布(LDA)主题挖掘方法和支持向量机(SVM)分类算法,构建基于社交媒体的暴雨灾情信息挖掘模型,收集2019年“4·11深圳暴雨”的微博数据,识别出4类微博文本主题,并进一步对“灾情信息”这一主题进行二次挖掘。结果显示,所构建的暴雨灾情信息挖掘模型能够准确识别出微博文本中蕴含的灾情信息,提取了深圳暴雨事件带来的交通影响、人员伤亡、建筑物倒塌、积水、停电、停水6种灾情信息;暴雨灾情信息挖掘和分析的结果能准确反映灾害的发展状况,在时间上,挖掘出的灾情信息实时反映了灾害的发展过程,在空间上,微博数量集中的地区与灾情发生地点保持一致,基于社交媒体的灾情信息实时获取方法能够为政府开展应急救援及救灾部署工作提供实时基础信息支持。

    Abstract:

    During the process of rainstorm, obtaining the real-time rainstorm disaster information, especially the effects and consequences of the disaster, is of great significance to the timely response and evaluation of the rainstorm disaster. Combining the latent Dirichlet allocation(LDA)and the supported vector machine(SVM), a mining model forrainstorm disaster information based on social media is proposed. The rainstorm event on April 11, 2019 is selected as an example, and 10 015 Weibo texts about “Shenzhen rainstorm” are collected. Four types of Weibo text themesare identified, and after its secondary mining, six kinds of disaster information as well as the temporal and spatial differences are obtained. The results show that the proposed mining model forrainstorm disaster information performs well on the identification of disaster information of the Weibo texts, and extract six kinds of disaster information of Shenzhen rainstorm: traffic congestion, casualty, building collapse, ponding, power failure and water outage. The mining and analysis results of rainstorm disaster information can accurately reflect the development status of the disaster. The temporal results of disaster information reflect its real-time development, and spatial results indicate that the area with concentrated Weibo texts agrees with that with occurrence of the disaster. The proposed mining approach for rainstorm disaster information may provide the basic and real-time information support for emergency rescue and relief work.

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黄晶,李梦晗,康晋乐,等.基于社交媒体的暴雨灾情信息实时挖掘与分析——以2019年“4·11深圳暴雨”为例[J].水利经济,2021,39(2):86-94.(HUANG Jing, LI Menghan, KANG Jinle, et al. Mining and analysis of rainstorm disaster information based on social media—Case study of Shenzhen rainstorm on April 11, 2019[J]. Journal of Economics of Water Resources,2021,39(2):86-94.(in Chinese))

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  • 收稿日期:2020-05-27
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  • 在线发布日期: 2021-04-14
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