Recent advances in computational science and data-intensive methods are significantly improving our ability to detect, model, and respond to natural hazards in real/near-real time. From earthquakes, tsunamis and floods to wildfires, volcanic eruptions, and extreme weather events, the integration of HPC, predictive modeling, and intelligent systems is enabling more effective and timely emergency response and operational frameworks and services, as illustrated from the outcomes of several EU-funded projects (e.g. ChEESE, doi: 10.3030/101093038; DT-GEO, doi:10.3030/101058129; GANANA, doi:10.3030/101196247).
This session focuses on the role of scalable, adaptive, and AI-enhanced computing approaches in supporting the entire natural hazard management cycle: from early detection and warning to modelling, impact forecasting and decision support. We invite contributions that explore but not limited to innovative methods and real-world applications across the areas such as: (i) Early detection and rapid warning systems, leveraging sensor networks, remote sensing, and predictive analytics, (ii)Time-critical simulations and forecasting models, (iii) AI applications in natural hazard contexts, including real-time/near real-time earthquake signal analysis, landslide and wildfire risk mapping, flood extent detection, and uncertainty-aware forecasting using ML-based ensemble models, (iv) Operational platforms and decision-support tools, integrating real-time data streams with adaptive modeling, (v) Climate change impacts on hydro-geological hazards, with a focus on floods, landslides, and droughts, (vi) Physics-informed learning and the integration of climate scenarios, (vii) AI-driven coupled hazard modeling using multi-source data, (viii) Representation of hydrological interactions among atmosphere, vegetation, and soil, and, (ix) Case studies demonstrating the application of such methods etc.
We invite contributions that showcase novel approaches in computational science, AI / machine learning, modeling systems, or hybrid workflows that improve readiness and responsiveness during natural disasters. We particularly encourage interdisciplinary submissions that highlight collaborative work across geoscience, computer science, and emergency management. This session aims to bring together researchers, practitioners, and system developers working at the intersection of geoscience and urgent computing to advance the state of natural hazard mitigation and civil protection.