EGU22-7129
https://doi.org/10.5194/egusphere-egu22-7129
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

The Samos earthquake event (Mw = 7, 30 October 2020, Greece) as case study for applying machine learning on texts and photos scraped from social networks for developing seismic intensity maps.

Stathis G Arapostathis
Stathis G Arapostathis
  • self employed, a little map, Chalandri-Athens, Greece (e.arapostathis@gmail.com)

Main purpose of current research article is to present latest findings on automatic methods of manipulating social network data for developing seismic intensity maps. As case study the author selected the 2020 Samos earthquake event (Mw= 7, 30 October 2020, Greece). That earthquake event had significant consequences to the urban environment along with 2 deaths and 19 injuries. Initially an automatic approach, presented recently in the international literature was applied producing thus seismic intensity maps from tweets. Furthermore, some initial findings regarding the use of machine learning in various parts of the automatic methodology were presented along with potential of using photos posted in social networks. The data used were several thousands tweets and instagram posts.The results, provide vital findings in enriching data sources, data types, and effective rapid processing.

How to cite: Arapostathis, S. G.: The Samos earthquake event (Mw = 7, 30 October 2020, Greece) as case study for applying machine learning on texts and photos scraped from social networks for developing seismic intensity maps., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7129, https://doi.org/10.5194/egusphere-egu22-7129, 2022.

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