Today we are using an unprecedented wealth of social media platforms to generate and share information regarding a wide class of events, which include extreme meteorological conditions and natural hazards such as floods. This paper proposes an automated set of services that start from the availability of weather forecasts, including both an event detection technique and a selective information retrieval from on-line social media. The envisioned services aim to provide qualitative feedback for meteorological models, detect the occurrence of an emergency event and extract informative content that can be used to complement the situational awareness. We implement such services and evaluate them during a recent weather induced flood. Our approach could be highly beneficial for monitoring agencies and meteorological offices, who act in the early warning phase, and also for authorities and first responders, who manage the emergency response phase.

Early detection and information extraction for weather-induced floods using social media streams

M. Cristoforetti;A. Alikadic
2018

Abstract

Today we are using an unprecedented wealth of social media platforms to generate and share information regarding a wide class of events, which include extreme meteorological conditions and natural hazards such as floods. This paper proposes an automated set of services that start from the availability of weather forecasts, including both an event detection technique and a selective information retrieval from on-line social media. The envisioned services aim to provide qualitative feedback for meteorological models, detect the occurrence of an emergency event and extract informative content that can be used to complement the situational awareness. We implement such services and evaluate them during a recent weather induced flood. Our approach could be highly beneficial for monitoring agencies and meteorological offices, who act in the early warning phase, and also for authorities and first responders, who manage the emergency response phase.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/313811
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