• Intelligent architectures for extreme event visualisation

Song, Yang ORCID: https://orcid.org/0000-0003-1283-1672, Pagnucco, Maurice ORCID: https://orcid.org/0000-0001-7712-6646, Wu, Frank, Asadipour, Ali ORCID: https://orcid.org/0000-0003-0159-3090 and Ostwald, Michael J. ORCID: https://orcid.org/0000-0001-6210-6984, 2024, Book Section, Intelligent architectures for extreme event visualisation In: Del Favero, Dennis, Thurow, Susanne, Ostwald, Michael J. and Frohne, Ursula, (eds.) Climate Disaster Preparedness. Arts, Research, Innovation and Society (ARIS) . Springer, pp. 37-48. ISBN 978-3-031-56114-6

Abstract or Description:

Realistic immersive visualisation can provide a valuable method for studying extreme events and enhancing our understanding of their complexity, underlying dynamics and human impacts. However, existing approaches are often limited by their lack of scalability and incapacity to adapt to diverse scenarios. In this chapter, we present a review of existing methodologies in intelligent visualisation of extreme events, focusing on physical modelling, learning-based simulation and graphic visualisation. We then suggest that various methodologies based on deep learning and, particularly, generative artificial intelligence (AI) can be incorporated into this domain to produce more effective outcomes. Using generative AI, extreme events can be simulated, combining past data with support for users to manipulate a range of environmental factors. This approach enables realistic simulation of diverse hypothetical scenarios. In parallel, generative AI methods can be developed for graphic visualisation components to enhance the efficiency of the system. The integration of generative AI with extreme event modelling presents an exciting opportunity for the research community to rapidly develop a deeper under-standing of extreme events, as well as the corresponding preparedness, response and management strategies.

Official URL: https://link.springer.com/book/10.1007/978-3-031-5...
Subjects: Other > Mathematical and Computer Sciences > G400 Computer Science
Other > Mathematical and Computer Sciences > G600 Software Engineering
Other > Mathematical and Computer Sciences > G700 Artificial Intelligence
School or Centre: Research Centres > Computer Science Research Centre
Research & Innovation
Identification Number or DOI: https://doi.org/10.1007/978-3-031-56114-6_4
Date Deposited: 20 May 2024 15:05
Last Modified: 20 May 2024 15:05
URI: https://researchonline.rca.ac.uk/id/eprint/5848
Edit Item (login required) Edit Item (login required)