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Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring

Author

Listed:
  • Clemens Havas

    (Paris-Lodron University of Salzburg)

  • Bernd Resch

    (Paris-Lodron University of Salzburg
    Harvard University)

Abstract

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.

Suggested Citation

  • Clemens Havas & Bernd Resch, 2021. "Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(3), pages 2939-2969, September.
  • Handle: RePEc:spr:nathaz:v:108:y:2021:i:3:d:10.1007_s11069-021-04808-4
    DOI: 10.1007/s11069-021-04808-4
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    References listed on IDEAS

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    1. J. F. Rosser & D. G. Leibovici & M. J. Jackson, 2017. "Rapid flood inundation mapping using social media, remote sensing and topographic data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 103-120, May.
    2. Alison Abbott & Quirin Schiermeier, 2016. "Italian scientists shocked by earthquake devastation," Nature, Nature, vol. 537(7618), pages 15-16, September.
    3. Xiangyang Guan & Cynthia Chen, 2014. "Using social media data to understand and assess disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 837-850, November.
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    Cited by:

    1. Hartwig H. Hochmair & Gerhard Navratil & Haosheng Huang, 2023. "Perspectives on Advanced Technologies in Spatial Data Collection and Analysis," Geographies, MDPI, vol. 3(4), pages 1-5, November.

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