References:
[1] Q. Brissaud, and E. Astafyeva. Near-real-time detection of co-seismic ionospheric disturbances using machine learning. Geophysical Journal International (in review, 2021).
https://doi.org/10.1002/essoar.10507674.1
A recent bulletin from the World Meteorological Organization highlighted our work on near-real-time earthquake detection using GNSS data. Summary and bulletin as pdf accessible below:
https://public.wmo.int/en/resources/bulletin/artificial-intelligence-disaster-risk-reduction-opportunities-challenges-and
Artificial intelligence (AI), in particular machine learning (ML), is playing an increasingly important role in disaster risk reduction (DRR) – from the forecasting of extreme events and the development of hazard maps to the detection of events in real time, the provision of situational awareness and decision support, and beyond. This raises several questions: What opportunities does AI present? What are the challenges? How can we address the challenges and benefit from the opportunities? And, how can we use AI to provide important information to policy-makers, stakeholders, and the public to reduce disaster risks? In order to realize the potential of AI for DRR and to articulate an AI for DRR strategy, we need to address these questions and forge partnerships that drive AI in DRR forward.