Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.


Volcano Hazard Monitoring using Statistical Methods and Machine Learning

Monitoring of volcanic hazards presents extraordinarily challenging problems, from detecting and quantifying hazardous phenomena during eruptive events to forecasting their impact to assess risks to people and property. Helping address these problems, however, is an abundance of satellite and ground-based data-sets with ever‐improving temporal, spatial, and spectral resolutions that are mostly open and publicly available. This exceptional combination of pressing challenges and abundant data is leading to the growing use of statistical analysis and methods of artificial intelligence (AI) to solve problems of volcanic hazards. Machine learning, a type of AI in which computers learn from data, is gaining importance in volcanology, not only for monitoring purposes (i.e., in real-time) but also for later hazards analysis (e.g. modelling tools). This session welcomes contributions that cross-fertilize efforts in traditional ground-based volcano monitoring systems with technological innovation from satellite remote sensing and advanced statistical methods and machine learning for developing a better understanding of volcanic hazards.

Co-organized by , co-sponsored by AGU
Convener: Ciro Del Negro | Co-conveners: Eleonora AmatoECSECS, Claudia CorradinoECSECS, Federica TorrisiECSECS