Integrating Digital Technologies and eXplainable AI (XAI) in Natural Hazard and Disaster Management
Co-organized by ESSI1
Convener:
Paraskevas Tsangaratos
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Co-conveners:
Raffaele Albano,
Ioanna Ilia,
Haoyuan Hong,
Elena Xoplaki,
Ivanka Pelivan,
Yi Wang
Orals
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Tue, 16 Apr, 08:30–09:55 (CEST) Room 0.15
Posters on site
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Attendance Tue, 16 Apr, 10:45–12:30 (CEST) | Display Tue, 16 Apr, 08:30–12:30 Hall X4
Posters virtual
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Attendance Tue, 16 Apr, 14:00–15:45 (CEST) | Display Tue, 16 Apr, 08:30–18:00 vHall X4
We place a special emphasis on the role of eXplainable AI (XAI) in demystifying AI-driven predictive models. By exploring algorithms like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), we aim to make AI predictions in natural hazard assessment transparent and trustworthy. This approach not only enhances the predictive accuracy but also fosters trust and understanding among stakeholders.
Attendees will gain insights into cutting-edge research and practical applications, showcasing how these integrated technologies enable real-time monitoring, early warning systems, and effective communication strategies for disaster management. The session will feature case studies highlighting the successful application of these technologies in diverse geographic regions and hazard scenarios. This interdisciplinary platform is dedicated to advancing our capabilities in mitigating the risks and impacts of natural hazards, paving the way for safer, more resilient communities in the face of increasing environmental challenges.
08:30–08:35
5-minute convener introduction
08:35–08:45
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EGU24-16
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ECS
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Virtual presentation
08:45–08:55
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EGU24-1408
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ECS
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Highlight
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Virtual presentation
08:55–09:05
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EGU24-9063
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ECS
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On-site presentation
09:05–09:15
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EGU24-12368
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On-site presentation
09:15–09:25
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EGU24-22176
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On-site presentation
09:25–09:35
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EGU24-15738
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ECS
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Highlight
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Virtual presentation
09:35–09:45
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EGU24-16528
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ECS
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On-site presentation
09:45–09:55
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EGU24-7006
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ECS
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Virtual presentation
X4.106
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EGU24-20549
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Highlight