Research on the Application of Topic Models Based on Geological Disaster Information Mining
Geological disasters, as a common occurrence, have a serious impact on social development in terms of their frequency of occurrence, disaster effects, and resulting losses. To effectively reduce the casualties, property losses, and social effects caused by various disasters, it is necessary to condu...
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MDPI AG
2024-12-01
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| author | Gang Cheng Qinliang You Gangqiang Li Youcai Li Daisong Yang Jinghong Wu Yaxi Wu |
| author_facet | Gang Cheng Qinliang You Gangqiang Li Youcai Li Daisong Yang Jinghong Wu Yaxi Wu |
| author_sort | Gang Cheng |
| collection | DOAJ |
| description | Geological disasters, as a common occurrence, have a serious impact on social development in terms of their frequency of occurrence, disaster effects, and resulting losses. To effectively reduce the casualties, property losses, and social effects caused by various disasters, it is necessary to conduct real-time monitoring and early warning of various geological disaster risks. With the growing development of the information age, public attention to disaster relief, casualties, social impact effects, and other related situations has been increasing. Since social media platforms such as Weibo and Twitter contain a vast amount of real-time data related to disaster information before and after a disaster occurs, scientifically and effectively utilizing these data can provide sufficient and reliable information support for disaster relief, post-disaster recovery, and public appeasement efforts. As one of the techniques in natural language processing, the topic model can achieve precise mining and intelligent analysis of valuable information from massive amounts of data on social media to achieve rapid use of thematic models for disaster analysis after a disaster occurs, providing reference for post-disaster-rescue-related work. Therefore, this article first provides an overview of the development process of the topic model. Secondly, based on the technology utilized, the topic models were roughly classified into three categories: traditional topic models, word embedding-based topic models, and neural network-based topic models. Finally, taking the disaster data of “Dongting Lake breach” in Hunan, China as the research object, the application process and effectiveness of the topic model in urban geological disaster information mining were systematically introduced. The research results provide important references for the further practical innovation and expansion of the topic model in the field of disaster information mining. |
| format | Article |
| id | doaj-art-cfcb518588fb43d0976f88cc14be8b1e |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Information |
| spelling | doaj-art-cfcb518588fb43d0976f88cc14be8b1e2024-12-27T14:30:50ZengMDPI AGInformation2078-24892024-12-01151279510.3390/info15120795Research on the Application of Topic Models Based on Geological Disaster Information MiningGang Cheng0Qinliang You1Gangqiang Li2Youcai Li3Daisong Yang4Jinghong Wu5Yaxi Wu6School of Computer Science, North China Institute of Science and Technology, Beijing 101601, ChinaSchool of Computer Science, North China Institute of Science and Technology, Beijing 101601, ChinaSchool of Mine Safety, North China Institute of Science and Technology, Beijing 101601, ChinaSchool of Computer Science, North China Institute of Science and Technology, Beijing 101601, ChinaSchool of Computer Science, North China Institute of Science and Technology, Beijing 101601, ChinaSchool of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215011, ChinaSchool of Computer Science, North China Institute of Science and Technology, Beijing 101601, ChinaGeological disasters, as a common occurrence, have a serious impact on social development in terms of their frequency of occurrence, disaster effects, and resulting losses. To effectively reduce the casualties, property losses, and social effects caused by various disasters, it is necessary to conduct real-time monitoring and early warning of various geological disaster risks. With the growing development of the information age, public attention to disaster relief, casualties, social impact effects, and other related situations has been increasing. Since social media platforms such as Weibo and Twitter contain a vast amount of real-time data related to disaster information before and after a disaster occurs, scientifically and effectively utilizing these data can provide sufficient and reliable information support for disaster relief, post-disaster recovery, and public appeasement efforts. As one of the techniques in natural language processing, the topic model can achieve precise mining and intelligent analysis of valuable information from massive amounts of data on social media to achieve rapid use of thematic models for disaster analysis after a disaster occurs, providing reference for post-disaster-rescue-related work. Therefore, this article first provides an overview of the development process of the topic model. Secondly, based on the technology utilized, the topic models were roughly classified into three categories: traditional topic models, word embedding-based topic models, and neural network-based topic models. Finally, taking the disaster data of “Dongting Lake breach” in Hunan, China as the research object, the application process and effectiveness of the topic model in urban geological disaster information mining were systematically introduced. The research results provide important references for the further practical innovation and expansion of the topic model in the field of disaster information mining.https://www.mdpi.com/2078-2489/15/12/795geological disastersinformation miningtopic modelapplied researchvisual analysis |
| spellingShingle | Gang Cheng Qinliang You Gangqiang Li Youcai Li Daisong Yang Jinghong Wu Yaxi Wu Research on the Application of Topic Models Based on Geological Disaster Information Mining Information geological disasters information mining topic model applied research visual analysis |
| title | Research on the Application of Topic Models Based on Geological Disaster Information Mining |
| title_full | Research on the Application of Topic Models Based on Geological Disaster Information Mining |
| title_fullStr | Research on the Application of Topic Models Based on Geological Disaster Information Mining |
| title_full_unstemmed | Research on the Application of Topic Models Based on Geological Disaster Information Mining |
| title_short | Research on the Application of Topic Models Based on Geological Disaster Information Mining |
| title_sort | research on the application of topic models based on geological disaster information mining |
| topic | geological disasters information mining topic model applied research visual analysis |
| url | https://www.mdpi.com/2078-2489/15/12/795 |
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