AI for Disaster Risk Management

Of Regional scope.

"In the context of Climate Change, extreme weather-induced events are expected to increase in frequency and intensity. In this sense, Early Warning Systems (EWS) have been identified as a crucial instrument to trigger actions supporting situational awareness and rapid response in front of weather-related emergencies. International organizations (such as thw UN, WMO, and UNDRR) are promoting the development and implementation of Impact-based EWS (IEWS) adapted to the local needs of authorities, first responders and the population. IEWS are oriented to anticipate the actual impact of extreme weather on people, infrastructures and critical services and elements than the meteorological extremes themselves. Artificial Intelligence and Machine Learning techniques can identify patterns in meteorological data that may relate to impacts with higher anticipation than traditional methods. In this project, highly detailed real-life impact data as well as a wide variety of meteorological inputs will be used to the recognition of these types of patterns and to provide significant improvement in impact early warning. "

Researchers

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Andreas

Kaltenbrunner

Leader

Àgata

Lapedriza Garcia

Leader