The potential for the use of model fusion techniques in building and developing catastrophe models
K. R. Royse, J. K. Hillier, A. Hughes, A. Kingdon, A. Singh, L. Wang, 2017. "The potential for the use of model fusion techniques in building and developing catastrophe models", Integrated Environmental Modelling to Solve Real World Problems: Methods, Vision and Challenges, A. T. Riddick, H. Kessler, J. R. A. Giles
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Global economic losses related to natural hazards are large and increasing, peaking at US$380 billion in 2011 driven by earthquakes in Japan and New Zealand and flooding in Thailand. Catastrophe models are stochastic event-set based computer models, first created 25 years ago, that are now vital to risk assessment within the insurance and reinsurance industry. They estimate likely losses from extreme events, whether natural or man-made. Most catastrophe models limit the level of user interaction, stereotyped as ‘black boxes’. In this paper we investigate how model fusion techniques could be used to develop ‘plug and play’ catastrophe models and discuss the impact of open access modelling on the insurance industry and other stakeholders (e.g. local government).
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Integrated Environmental Modelling to Solve Real World Problems: Methods, Vision and Challenges
The discipline of Integrated Environmental Modelling (IEM) has developed in order to solve complex environmental problems, for example understanding the impacts of climate change on the physical environment. IEM provides methods to fuse or link models together, this in turn requires facilities to make models discoverable and also to make the outputs of modelling easily visualized.
The vision and challenges for IEM going forward are summarized by leading proponents. Several case studies describe the application of model fusion to a range of real-world problems including integrating groundwater and recharge models within the UK Environment Agency, and the development of ‘catastrophe’ models to predict better the impact of natural hazards. Communicating modelling results to end users who are often not specialist modellers is also an emerging area of research addressed within the volume. Also included are papers that highlight current developments of the technology platforms underpinning model fusion.