气候变化领域集成服务门户(CCPortal): Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring

CCPortal

DOI	10.1007/s11069-021-04808-4
	Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring
	Havas C.; Resch B.
发表日期	2021
ISSN	0921030X
起始页码	2939
结束页码	2969
卷号	108 期号:3
英文摘要	Up-to-date information about an emergency is crucial for effective disaster management. However, severe restrictions impede the creation of spatiotemporal information by current remote sensing-based monitoring systems, especially at the beginning of a disaster. Multiple publications have shown promising results in complementing monitoring systems through spatiotemporal information extracted from social media data. However, various monitoring system criteria, such as near-real-time capabilities or applicability for different disaster types and use cases, have not yet been addressed. This paper presents an improved version of a recently proposed methodology to identify disaster-impacted areas (hot spots and cold spots) by combining semantic and geospatial machine learning methods. The process of identifying impacted areas is automated using semi-supervised topic models for various kinds of natural disasters. We validated the portability of our approach through experiments with multiple natural disasters and disaster types with differing characteristics, whereby one use case served to prove the near-real-time capability of our approach. We demonstrated the validity of the produced information by comparing the results with official authority datasets provided by the United States Geological Survey and the National Hurricane Centre. The validation shows that our approach produces reliable results that match the official authority datasets. Furthermore, the analysis result values are shown and compared to the outputs of the remote sensing-based Copernicus Emergency Management Service. The information derived from different sources can thus be considered to reliably detect disaster-impacted areas that were not detected by the Copernicus Emergency Management Service, particularly in densely populated cities. © 2021, The Author(s).
关键词	Disaster management Geospatial analysis Machine learning Semantic topic analysis Social media
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206268
作者单位	Department of Geoinformatics, Paris-Lodron University of Salzburg, Salzburg, 5020, Austria; Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, United States
推荐引用方式 GB/T 7714	Havas C.,Resch B.. Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring[J],2021,108(3).
APA	Havas C.,&Resch B..(2021).Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring.Natural Hazards,108(3).
MLA	Havas C.,et al."Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring".Natural Hazards 108.3(2021).