热带地理2024，Vol.44Issue(6)：1079-1089,11.DOI:10.13284/j.cnki.rddl.003882
基于微博文本和深度学习的台风灾情识别方法研究
Research on Typhoon Damage Identification Method Based on Weibo Texts and Deep Learning:A Case Study of Typhoons Crossing Guangdong Province from 2010 to 2019
摘要
Abstract
Typhoons are extreme weather phenomena that seriously affect the daily lives of residents and regular functioning of society.As one of the most typhoon-prone countries in the world,China is constantly affected by typhoons and their secondary disasters,which can cause significant casualties and economic losses.The extent of damage caused by typhoons is inversely proportional to the effectiveness of the emergency response.Therefore,accurate and comprehensive access to damage information is critical for rescue and recovery.Social media,which is characterized by low collection costs and rich content,is an important means of collecting disaster information.With the development of social media,it has become increasingly important to accurately and comprehensively identify social media texts related to typhoons.In this study,by combining typhoon attribute data and a multi-label classification method with Bidirectional Encoder Representations from Transformers(BERT)and Bidirectional Long Short-Term Memory(BiLSTM)models,a typhoon damage identification method based on Weibo texts and deep learning is proposed to identify the damage caused by severe and super typhoons that made landfall in Guangdong Province from 2010 to 2019.First,texts related to typhoon damage were identified from the massive Weibo texts and further classified into five damage categories:transportation,public,electricity,forestry,and waterlogging.The typhoon damage characteristics were comparatively analyzed using spatial distribution,time curves,and quantity curves.The results showed that the accuracy of typhoon damage classification was high,with an F1 score of 0.907 for identifying typhoon damage-related texts and 0.814 for further classifying them into five damage categories.Typhoon attribute data and multi-label classification methods have improved the accuracy and comprehensiveness of typhoon damage identification.Compared to the use of Weibo texts only and the single-label classification method,typhoon attribute data provide information on the geographic context of the typhoon at the time of the texts'release,and the multi-label classification method allows the texts to belong to more than one damage category.This study shows that there are differences in the proportion of damage caused by different typhoons,which are related to the intensity and track of the typhoon,as well as the development level of the affected areas.In addition,before the typhoon makes landfall,precautions lead to transportation and public-related damage.After the typhoon makes landfall,the typhoon damage shows single and double-peak characteristics,and the different characteristics reflect the changing trends and features of typhoon damage.This study provides a scientific basis for typhoon damage identification and disaster relief in Guangdong Province.关键词
台风灾情/微博文本/深度学习/BERT/BiLSTM/多标签分类/广东省Key words
typhoon damage/Weibo texts/deep learning/BERT/BiLSTM/multi-label classification/Guangdong Province分类
信息技术与安全科学引用本文复制引用
邹黎威,贺智,周承乐..基于微博文本和深度学习的台风灾情识别方法研究[J].热带地理,2024,44(6):1079-1089,11.基金项目
国家自然科学基金面上项目(42271325) （42271325）
广东省城市安全智能监测与智慧城市规划企业重点实验室资助项目(GPK-LIUSMSCP-2023-KF-02) （GPK-LIUSMSCP-2023-KF-02）
南方海洋科学与工程广东省实验室(珠海)创新团队建设项目(311022018) （珠海）
