Disaster information extraction and analysis of severe convective weather based on Weibo: A case study of severe wind-hail event on April 30, 2021 in Jiangsu

Abstract

[Objective] Severe convective weather is typically characterized by abrupt onset and localized impact. Effectively acquiring disaster information from social media can compensate for the insufficient observation density of severe convective weather and provide information support for disaster emergency management. [Methods] Chinese text segmentation and statistical analysis were performed on text data of typical severe convective weather. By integrating meteorological expert knowledge, a disaster-themed corpus tailored to severe convective weather was developed. Semantic information was incorporated into the latent Dirichlet allocation(LDA) topic model and the support vector machine(SVM) classification algorithm to construct a disaster information extraction model under severe convective weather. Taking the severe wind-hail event in Jiangsu on April 30, 2021 as an example, 16 334 original Weibo text messages were collected for simulation experiments. [Results] (1) The constructed disaster information extraction model for severe convective weather demonstrated remarkable effectiveness in identifying and classifying disaster information in Weibo texts. Through primary topic mining, five themes were extracted: weather conditions, public education on disaster prevention, disaster impact, rescue requests, and other information. The secondary classification was performed on “disaster impact” to extract six specific categories: public facilities, power and communication, vehicle traffic, agricultural facilities, casualties, and others. Cross-validation revealed an average accuracy of 92.70% for the primary classification and 90.95% for the secondary classification.(2) The development process of severe convective weather was divided into three stages: warning stage, outbreak stage, and post-disaster stage. All information categories peaked during the outbreak stage. During the outbreak stage, information on public facilities and power and communication was the most prevalent, while during the post-disaster stage, discussions about casualties were the most frequent.(3) The spatial distribution of disaster information quantity was generally consistent with the regions severely affected by the disaster. During severe convective weather events, public facilities faced a higher risk of exposure with damage being the most common. [Conclusion] The extraction model of disaster information based on Weibo for severe convective weather can effectively extract implicit disaster information in Weibo texts, reflect the variation characteristics of disaster events and the focus of public opinion, and provide valuable reference for disaster monitoring and early warning services as well as emergency response command.

Keywords

severe convective weather / disaster information / social media / Weibo / information extraction / topic model / text classification / rainfall