Information collected from warning systems monitoring natural threats can be synthesized into a risk measure to determine the state of nature, and for defining the threshold for issuing (or not) an early warning. This means that a change in the risk measure can trigger the implementation of countermeasures for reducing the hazard, or for reducing the vulnerability and the consequences. Associated costs in the implementation of either should be added to the risk measure, so that the updated risk can be used as the index defining the corresponding warning level.
This work introduces the case of a pre-reliability analysis of the tsunamigenic rockslide at Åknes, Norway. For this purpose, information gathered from engineers, geologists and other stakeholders are incorporated into a probability template based on inference diagrams, which allow for representing not only causal associations between events (represented by nodes) taking place from the threat triggering factors up to the definition of the risk measure. Some of these include key events such as the threats triggering factors, the threats themselves (rockslide and tsunami), the effect of the early warning system, and others. In addition, it is necessary to define how these events interact with each other, which should be stated through the definition of probability distributions.
Once the information representing the different events is gathered, it can be tailored into a directed acyclic graph, making a model which graphically is intuitive to follow. In this way, it is possible to trace the passing of information through the network by making use of the dependencies defined for each probability distribution. This is possible either through stochastic transformation or by letting a set of stochastic variables be a part of a second level of parameters (hyper-parameters). Meaning that it is possible to create complex information structures using simple representations, which help to enhance inferences about the risk measures.
After constructing the network, the major challenge is to find optimal thresholds for assigning the warning level. In this paper, we introduce Monte Carlo simulation techniques to propagate states of probability, so that warning thresholds can vary to identify optimal risk measures representing warning thresholds with the lowest expected consequence.
The next phase of this research is to collect and process information from the Åknes site in real time. Here we incorporate Bayesian probability to the network, making a Bayesian Network. It permits us to update information in any node in the network and propagate it back and forth on different directions according to the presence of new evidence at any time. This represents a pre-reliability analysis, which can help to tune the decision-making associated to the implementation of an Early Warning System.