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清华大学学报(自然科学版)  2022, Vol. 62 Issue (10): 1626-1635    DOI: 10.16511/j.cnki.qhdxxb.2022.22.046
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基于微博数据的自然灾害应急救助需求评估
周义棋1, 田向亮2, 钟茂华1
1. 清华大学 工程物理系, 公共安全研究院, 北京 100084;
2. 中国安全生产科学研究院 矿山安全技术研究所, 北京 100012
Assessment of natural disaster emergency relief demand based on Microblog data
ZHOU Yiqi1, TIAN Xiangliang2, ZHONG Maohua1
1. Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China;
2. Mine Safety Technology Institute, China Academy of Safety Science and Technology, Beijing 100012, China
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摘要 结合实时更新的社交媒体数据进行应急救助需求分析,有助于提高灾害应急救助过程中的时效性和准确性。为掌握受灾区域的实际救灾需求,该文从应急救援及指挥保障需求、灾后紧急救援需求、基本生活保障需求和公共基础设施保障需求4个方面构建应急救助需求紧迫度分级评估指标体系,提出了基于组合赋权法和灰色改进逼近理想解排序法(TOPSIS)的受灾区域应急救助需求紧迫度分级评估模型。以台风"利奇马"为例,对受灾省份各地级市的应急救助具体需求进行评估,结合灾害损失数据验证了所提出的分级评估模型的有效性。
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周义棋
田向亮
钟茂华
关键词 应急救助微博网络舆情需求评估自然灾害    
Abstract:Real-time social media data can be used to improve data timeliness and accuracy during disaster emergency responses.The key disaster area needs are prioritized here using a disaster relief index system for emergency rescue and command support needs,post-disaster emergency rescue needs,basic living support needs and public infrastructure support needs.The emergency relief evaluation model uses entropy weights and the grey improvement technique for order preference by similarity to an ideal solution (TOPSIS).The method is applied to the typhoon Lekima response as an example to assess the specific emergency rescue needs in the cities of affected provinces to verify the effectiveness of this disaster evaluation model by comparison with disaster loss data.
Key wordsemergency assistance    Microblog    network public opinion    needs assessment    natural disaster
收稿日期: 2022-05-11      出版日期: 2022-09-03
基金资助:钟茂华,研究员,E-mail:mhzhong@tsinghua.edu.cn
引用本文:   
周义棋, 田向亮, 钟茂华. 基于微博数据的自然灾害应急救助需求评估[J]. 清华大学学报(自然科学版), 2022, 62(10): 1626-1635.
ZHOU Yiqi, TIAN Xiangliang, ZHONG Maohua. Assessment of natural disaster emergency relief demand based on Microblog data. Journal of Tsinghua University(Science and Technology), 2022, 62(10): 1626-1635.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2022.22.046  或          http://jst.tsinghuajournals.com/CN/Y2022/V62/I10/1626
  
  
  
  
  
  
  
  
  
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