EARS
EARS (Earthquake Alert and Report System): a Real Time Decision Support System for Earthquake Crisis Management
Marco Avvenuti
Stefano Cresci
Andrea Marchetti
Department of Information
Engineering
University of Pisa, Pisa, Italy
Institute for Informatics and
Telematics (IIT)
National Research Council
(CNR), Pisa, Italy
Institute for Informatics and
Telematics (IIT)
National Research Council
(CNR), Pisa, Italy
m.avvenuti@iet.unipi.it
stefano.cresci@iit.cnr.it andrea.marchetti@iit.cnr.it
Carlo Meletti
Maurizio Tesconi
National Institute of
Geophysics and Volcanology
Pisa, Italy
carlo.meletti@pi.ingv.it
Institute for Informatics and
Telematics (IIT)
National Research Council
(CNR), Pisa, Italy
maurizio.tesconi@iit.cnr.it
ABSTRACT
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. 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
Marco Avvenuti
Stefano Cresci
Andrea Marchetti
Department of Information
Engineering
University of Pisa, Pisa, Italy
Institute for Informatics and
Telematics (IIT)
National Research Council
(CNR), Pisa, Italy
Institute for Informatics and
Telematics (IIT)
National Research Council
(CNR), Pisa, Italy
m.avvenuti@iet.unipi.it
stefano.cresci@iit.cnr.it andrea.marchetti@iit.cnr.it
Carlo Meletti
Maurizio Tesconi
National Institute of
Geophysics and Volcanology
Pisa, Italy
carlo.meletti@pi.ingv.it
Institute for Informatics and
Telematics (IIT)
National Research Council
(CNR), Pisa, Italy
maurizio.tesconi@iit.cnr.it
ABSTRACT
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. 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