Abstract
Early warning systems help to mitigate the impact of disastrous natural catastrophes on society by providing short notice of an imminent threat to geographical regions. For early tsunami warning, real-time observations from a seismic monitoring network can be used to estimate the severity of a potential tsunami wave at a specific site. The ability of deriving accurate estimates of tsunami impact is limited due to the complexity of the phenomena and the uncertainties in seismic source parameter estimates. Here we describe the use of a Bayesian belief network (BBN), capable of handling uncertain and even missing data, to support emergency managers in extreme time critical situations. The BBN comes about via model selection from an artifically generated database. The data is generated by ancestral sampling of a generative model defined to convey formal expert knowledge and physical/mathematical laws known to hold in the realm of tsunami generation. Hence, the database implicitly holds the information for learning a BBN capturing the required domain knowledge.
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Blaser, L., Ohrnberger, M., Riggelsen, C., Scherbaum, F. (2009). Bayesian Belief Network for Tsunami Warning Decision Support. In: Sossai, C., Chemello, G. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2009. Lecture Notes in Computer Science(), vol 5590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02906-6_65
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DOI: https://doi.org/10.1007/978-3-642-02906-6_65
Publisher Name: Springer, Berlin, Heidelberg
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