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
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.
[1] FEKETE A. Societal resilience indicator assessment using demographic and infrastructure data at the case of Germany in context to multiple disaster risks[J]. International Journal of Disaster Risk Reduction, 2018, 31:203-211. [2] NAQVI A, MONASTEROLO I. Assessing the cascading impacts of natural disasters in a multi-layer behavioral network framework[J]. Scientific Reports, 2021, 11(1):20146. [3] CHENG C X, ZHANG T, SU K, et al. Assessing the intensity of the population affected by a complex natural disaster using social media data[J]. ISPRS International Journal of Geo-Information, 2019, 8(8):358. [4] HAN X H, WANG J L. Using social media to mine and analyze public sentiment during a disaster:A case study of the 2018 Shouguang City flood in China[J]. ISPRS International Journal of Geo-Information, 2019, 8(4):185. [5] 李绍攀,赵飞,周义棋,等.基于微博数据的台风灾害舆情分析与灾害损失估计[J].清华大学学报(自然科学版), 2022, 62(1):43-51. LI S P, ZHAO F, ZHOU Y Q, et al. Analysis of public opinion and disaster loss estimates from typhoons based on Microblog data[J]. Journal of Tsinghua University (Science and Technology), 2022, 62(1):43-51.(in Chinese) [6] 赵飞,廖永丰.突发自然灾害事件网络舆情传播特征及影响因素研究[J].地球信息科学学报, 2021, 23(6):992-1001. ZHAO F, LIAO Y F. Research on the dissemination characteristics and influencing factors of network public opinion of sudden natural disaster events[J]. Journal of Geo-Information Science, 2021, 23(6):992-1001.(in Chinese) [7] LEAL M, REIS E, PEREIRA S, et al. Physical vulnerability assessment to flash floods using an indicator-based methodology based on building properties and flow parameters[J]. Journal of Flood Risk Management, 2021, 14(3):e12712. [8] PAPATHOMA-KÖHLE M, SCHLÖGL M, FUCHS S. Vulnerability indicators for natural hazards:An innovative selection and weighting approach[J]. Scientific Reports, 2019, 9(1):15026. [9] XU S H, ZHANG M, MA Y, et al. Multiclassification method of landslide risk assessment in consideration of disaster levels:A case study of Xianyang City, Shaanxi Province[J]. ISPRS International Journal of Geo-Information, 2021, 10(10):646. [10] NGUYEN H X, NGUYEN A T, NGO A T, et al. A hybrid approach using GIS-based fuzzy AHP-TOPSIS assessing flood hazards along the south-central coast of Vietnam[J]. Applied Sciences, 2020, 10(20):7142. [11] 王莉芳.基于组合赋权与灰色改进TOPSIS方法的受灾点应急物质需求紧迫性分级评价[J].安全与环境工程, 2017, 24(6):94-100. WANG L F. Classification evaluation of urgency of disaster point emergency material demand based on combination weighting and improved TOPSIS method[J]. Safety and Environmental Engineering, 2017, 24(6):94-100.(in Chinese) [12] 王英,苏柏林,闫鹏,等.基于改进TOPSIS的受灾点需求紧迫性分级研究[J].安全与环境学报, 2019, 19(1):140-146. WANG Y, SU B L, YAN P, et al. Approach to the classification of the demand urgency of the affected points based on the improved TOPSIS[J]. Journal of Safety and Environment, 2019, 19(1):140-146.(in Chinese) [13] 诸克军,张新兰,肖荔瑾. Fuzzy AHP方法及应用[J].系统工程理论与实践, 1997, 17(12):64-69. ZHU K J, ZHANG X L, XIAO L J. The method and applications of fuzzy AHP[J]. Systems Engineering:Theory&Practice, 1997, 17(12):64-69.(in Chinese) [14] 周源,刘怀兰,杜朋朋,等.基于改进TF-IDF特征提取的文本分类模型研究[J].情报科学, 2017, 35(5):111-118. ZHOU Y, LIU H L, DU P P, et al. Research of text classification model based on the improved TF-IDF feature extraction[J]. Information Science, 2017, 35(5):111-118.(in Chinese) [15] 程启月.评测指标权重确定的结构熵权法[J].系统工程理论与实践, 2010, 30(7):1225-1228. CHENG Q Y. Structure entropy weight method to confirm the weight of evaluating index[J]. Systems Engineering:Theory&Practice, 2010, 30(7):1225-1228.(in Chinese) [16] DENG H P, YEH C H, WILLIS R J. Inter-company comparison using modified TOPSIS with objective weights[J]. Computers&Operations Research, 2000, 27(10):963-973. [17] 邓聚龙.灰色控制系统[M].武汉:华中理工大学出版社, 1985. DENG J L. Gray control system[M]. Wuhan:Huazhong University of Science&Technology Press, 1985.(in Chinese) [18] 向纯怡,高拴柱,刘达. 2021年西北太平洋和南海台风活动概述[J].海洋气象学报, 2022, 42(1):39-49. XIANG C Y, GAO S Z, LIU D. Overview of typhoon activities over western north Pacific and the South China Sea in 2021[J]. Journal of Marine Meteorology, 2022, 42(1):39-49.(in Chinese) [19] 周冠博,董林,王海平,等. 2020年西北太平洋和南海台风活动概述[J].海洋气象学报, 2021, 41(1):1-10. ZHOU G B, DONG L, WANG H P, et al. Overview of typhoon activities over western north Pacific and the South China Sea in 2020[J]. Journal of Marine Meteorology, 2021, 41(1):1-10.(in Chinese) [20] 王海平,董林. 2019年西北太平洋和南海台风活动概述[J].海洋气象学报, 2020, 40(2):1-9. WANG H P, DONG L. Overview of typhoon activities over western north Pacific and the South China Sea in 2019[J]. Journal of Marine Meteorology, 2020, 40(2):1-9.(in Chinese)