An organisation owned and operated by Cornell University, arXiv.org, have published a paper describing a prototype system that integrates social media analysis into the European Flood Awareness System (EFAS).
The authors of the report; Valerio Lorini, Carlos Castillo, Francesco Dottori, Milan Kalas, Domenco Nappo and Peter Salamon have developed software for EFAS named Social Media for Flood Risk (SMFR) which provides near-real-time information collected from social media about flood risks and impacts, including examples of messages in social media about it and integrated the data gathered back into EFAS.
In Europe, the Emergency Response Coordination Centre (ERCC), operating within the European Commission’s Civil Protection and Humanitarian Aid Operations department, was set up to support a fast and coordinated response to disasters both inside and outside Europe using resources from the countries participating in the EU Civil Protection Mechanism. This centre monitors hazards and risks, collects and analyzes real-time information on disasters, prepares plans for the deployment of experts, teams and equipment, and in general coordinates the EU’s disaster response efforts.
The European Flood Awareness System (EFAS) provides real-time information and forecasts about floods to the ERCC as well as to a series of partners including national and regional hydrological services. EFAS is part of the Copernicus Emergency Management Service (Copernicus EMS), and holds regularly updated flood-related
information such as probabilistic medium-range flood forecasts (including short-range flash floods), seasonal forecasts, and impact assessments and early warnings.
The Social Media for Flood Risk (SMFR) provides near-real-time information collected from social media about flood risks and impacts, including examples of messages in social media about it and displaying them back in the interface of EFAS.
This was funded by an Exploratory Research grant made available by the Joint Research Centre of the European Commission. La Caixa project LCF/PR/PR16/11110009 for partial support.
The full paper can be read here
Citation; arXiv:1904.10876 [cs.IR]