When a volcano erupts, providing information on hazardous volcanic phenomena, their effects to communities and enviornmentes, and the eruption's duration is crucial to inform risk mitigation strategies. However, eruptions are complex phenomena governed by interactions of many processes, which are often nonlinear and stochastic. Numerous uncertainties in the involved parameters make precise predictions of specific events in time and space usually unattainable; that is, volcanic eruptions can be intrinsically unpredictable. Despite these limitations, significant progress has been made in forecasting volcanic hazards and, in specific circumstances, in making predictions. Understanding and predicting volcanic phenomena requires a comprehensive approach that integrates satellite observations, field measurements, and advanced modelling techniques. This has led to the increased use of data-driven approaches, including artificial intelligence (AI) techniques, to address volcanic hazards. Looking to the future, AI models can be combined with physical constraints to bridge the gap between data-driven methods and physical modeling, thereby increasing the interpretability of AI predictions. This offers an alternative approach to dealing with the strongly nonlinear and time-dependent character of volcanic phenomena.
This multidisciplinary session seeks to bring together contributions focusing on enhancing traditional ground-based volcano monitoring systems through technological innovation in satellite remote sensing and computational methods, integrating deep-learning, data-driven, physical and statistical modelling approaches, to better understand and forecast volcanic hazards. By fostering discussions and sharing insights, we aim to drive forward the development of more comprehensive and integrated approaches to volcanic hazard assessment and risk mitigation.
Technologies for Forecasting Volcanic Hazards: Enhancing Risk Mitigation through Observations and Models
Co-organized by GMPV9, co-sponsored by
AGU
Convener:
Ciro Del Negro
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Co-conveners:
Alessio Alexiadis,
Eleonora AmatoECSECS,
Silvia Massaro,
Leonardo Mingari,
Pablo TierzECSECS,
Federica TorrisiECSECS