Abstract
Bayesian Networks (BNs) are probabilistic graphical models that provide a robust and flexible framework for understanding complex systems. Limited case studies have demonstrated the potential of BNs in modeling multiple data streams for eruption forecasting and volcanic hazard assessment. Nevertheless, BNs are not widely employed in volcano observatories. Motivated by their need to determine eruption-related fieldwork risks, we have worked closely with the New Zealand volcano monitoring team to appraise BNs for eruption forecasting with the purpose, at this stage, of assessing the utility of the concept rather than develop a full operational framework. We adapted a previously published BN for a pilot study to forecast volcanic eruption on Whakaari/White Island. Developing the model structure provided a useful framework for the members of the volcano monitoring team to share their knowledge and interpretation of the volcanic system. We aimed to capture the conceptual understanding of the volcanic processes and represent all observables that are regularly monitored. The pilot model has a total of 30 variables, four of them describing the volcanic processes that can lead to three different types of eruptions: phreatic, magmatic explosive and magmatic effusive. The remaining 23 variables are grouped into observations related to seismicity, fluid geochemistry and surface manifestations. To estimate the model parameters, we held a workshop with 11 experts, including two from outside the monitoring team. To reduce the number of conditional probabilities that the experts needed to estimate, each variable is described by only two states. However, experts were concerned about this limitation, in particular for continuous data. Therefore, they were reluctant to define thresholds to distinguish between states. We conclude that volcano monitoring requires BN modeling techniques that can accommodate continuous variables. More work is required to link unobservable (latent) processes with observables and with eruptive patterns, and to model dynamic processes. A provisional application of the pilot model revealed several key insights. Refining the BN modeling techniques will help advance understanding of volcanoes and improve capabilities for forecasting volcanic eruptions. We consider that BNs will become essential for handling ever-burgeoning observations, and for assessing data's evidential meaning for operational eruption forecasting.