Bayesian networks for tsunami early warning

Abstract

The various uncertainties in the earthquake-triggered tsunami threat assessment are difficult to quantify and/or integrate into the tsunami early warning process. Uncertainties in the (seismic) input parameters and the lack of knowledge about the earthquake slip distribution contribute most to the total uncertainty in real-time evaluated tsunami assessment. We present a method how to integrate and quantify these uncertainties in the warning process by evaluating a tsunami warning level probability distribution with a Bayesian network (BN) approach. As soon as an earthquake is detected, the seismic source parameter estimates are evaluated and a probabilistic overview on different tsunami warning levels is provided, feasible to support a decision maker at a warning center with important additional data. A BN system has been developed exemplarily for the region Sumatra. In this paper, we describe the method of BN generation by ancestral sampling, we critically analyse the assumptions made and weight the pro and cons of the BN approach. A case study demonstrates the workflow of the BN system in real-time and reveals the promising power of a BN analysis in the framework of early warning.