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New dimension added to modelling volcanic risk

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Palisade’s The DecisionTools Suite is enabling volcanologists to quantify the nature of one of the threats from the ‘Volcán de Fuego’ volcano in Guatemala. As an integrated set of programs for risk analysis and decision making under uncertainty, The DecisionTools Suite running in Microsoft Excel allows access to Monte Carlo simulation and other advanced analytics on the desktop.

Conventional risk assessments attempt to model the probability of a hazard and combine that with the vulnerability of the population, to create societal risk curves and estimated values of Individual Risk per Annum (IRPA). Knowing the potential number of deaths or cost from an eruption is not entirely useful, as little planning control or mitigation can be carried out.  In an attempt to increase the usefulness of the risk modelling, University of Bristol’s Environmental Risk Research Centre (BRISK) has looked at the vulnerability in a different way.

Normally volcanic risk assessments assume that the whole population is present in a location when a hazard hits. However, new work by BRISK has modelled the likelihood of a successful evacuation, using both @RISK and PrecisionTree, by inputting several variables obtained through a process of structured expert judgment. These variables, which include the time taken between a possible eruption and a possible hazard hitting a location, along with communication times from authorities and evacuation times, are each estimated with an uncertainty distribution.

The expert views are then weighted and pooled together. The variables are then constructed together in a logic tree within Palisade’s PrecisionTree, with the end node either being evacuation or no evacuation – and the probability of these outcomes being quantified, with their uncertainties. When fed back into the @RISK (hazard x vulnerability) model, the effects of a potential evacuation on the risk is very clear.

When looking in more detail at the model outputs from the logic tree, it became clear where the sensitivities were within the system. For example, it may be for a given location that the amount of time between a warning and the hazard hitting is crucial, or it may be that the time taken to evacuate is crucial. This new way of modelling volcanic risk informs better planning and more effective mitigation strategies.