Technologies for Forecasting Volcanic Hazards: From Remote Sensing to Modeling
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 10 May 2025 | Viewed by 2793
Special Issue Editors
Interests: physical volcanology; hazard assessment; remote sensing: artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; machine learning; volcano monitoring; satellite remote sensing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Forecasting volcanic hazards presents extraordinarily challenging problems, for reasons that span from the inherent complexity characterizing volcanic phenomena to the magnitude of associated risks. However, there is significant progress in forecasting volcanic hazards and, in specific circumstances, in making predictions. Improvements in forecasting are closely related to a wealth of data from enhanced monitoring techniques, such as satellite observations, and tremendous advances in computing power, leading to the increased use of data-driven approaches, including artificial intelligence (AI) techniques, 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). Looking to the future, AI models can be combined with physical constraints to bridge the gap between data-driven methods and physical modeling and to increase the interpretability of AI predictions, offering an alternative path to deal with the strongly nonlinear and time-dependent character of volcanic phenomena. This Special Issue invites contributions (original research articles and reviews are welcome) on the improvement of traditional ground-based volcano monitoring systems with technological innovation from satellite remote sensing, and of computational methods, blending deep-learning, data-driven approaches, and physics-based simulations, for developing a better understanding of volcanic hazards.
Dr. Ciro Del Negro
Dr. Claudia Corradino
Dr. Vito Zago
Guest Editors
Manuscript Submission Information
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Keywords
- data collection
- spatial analysis
- temporal analysis
- mathematical modeling
- sensitivity analysis
- validation and calibration
- uncertainty assessment
- decision support
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