2014 EARSearthquakeAlertandReportSys
- (Avvenuti et al., 2014) ⇒ Marco Avvenuti, Stefano Cresci, Andrea Marchetti, Carlo Meletti, and Maurizio Tesconi. (2014). “EARS (earthquake Alert and Report System): A Real Time Decision Support System for Earthquake Crisis Management.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623358
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Notes
Cited By
- http://scholar.google.com/scholar?q=%222014%22+EARS+%28earthquake+Alert+and+Report+System%29%3A+A+Real+Time+Decision+Support+System+for+Earthquake+Crisis+Management
- http://dl.acm.org/citation.cfm?id=2623330.2623358&preflayout=flat#citedby
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Author Keywords
- Data mining; decision support system; disaster management; event detection; human safety; social mining; social sensing
Abstract
Social sensing is based on the idea that communities or groups of people can provide a set of information similar to those obtainable from a sensor network. Emergency management is a candidate field of application for social sensing. In this work we describe the design, implementation and deployment of a decision support system for the detection and the damage assessment of earthquakes in Italy. Our system exploits the messages shared in real-time on Twitter, one of the most popular social networks in the world. Data mining and natural language processing techniques are employed to select meaningful and comprehensive sets of tweets. We then apply a burst detection algorithm in order to promptly identify outbreaking seismic events. Detected events are automatically broadcasted by our system via a dedicated Twitter account and by email notifications. In addition, we mine the content of the messages associated to an event to discover knowledge on its consequences. Finally we compare our results with official data provided by the National Institute of Geophysics and Volcanology (INGV), the authority responsible for monitoring seismic events in Italy. The INGV network detects shaking levels produced by the earthquake, but can only model the damage scenario by using empirical relationships. This scenario can be greatly improved with direct information site by site. Results show that the system has a great ability to detect events of a magnitude in the region of 3.5, with relatively low occurrences of false positives. Earthquake detection mostly occurs within seconds of the event and far earlier than the notifications shared by INGV or by other official channels. Thus, we are able to alert interested parties promptly. Information discovered by our system can be extremely useful to all the government agencies interested in mitigating the impact of earthquakes, as well as the news agencies looking for fresh information to publish.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2014 EARSearthquakeAlertandReportSys | Marco Avvenuti Stefano Cresci Andrea Marchetti Carlo Meletti Maurizio Tesconi | EARS (earthquake Alert and Report System): A Real Time Decision Support System for Earthquake Crisis Management | 10.1145/2623330.2623358 | 2014 |