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 the need to assess the probability of fatality when conducting field work, we have worked closely with the New Zealand volcano monitoring team to explore the potential utility of BNs. 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 eleven 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.
Highlights
Volcanoes are complex systems capable of producing hazardous phenomena that can kill or injure people and destroy assets, sometimes with little warning
We developed a pilot project for forecasting the probability of an eruption at Whakaari/White Island volcano, New Zealand
We briefly focus below on three areas that are important for future Bayesian networks (BNs) model development for eruption forecasting: continuous variables, dynamic system and expert involvement
Summary
Volcanoes are complex systems capable of producing hazardous phenomena that can kill or injure people and destroy assets, sometimes with little warning. Volcano observatories are responsible for monitoring volcanoes and interpreting data, often with the added responsibility of providing scientific information to authorities to assist with public safety and civil protection decisions. Quantitative support tools for eruption forecasting and decision-support are becoming crucially important for volcano observatories and monitoring groups (Selva et al, 2012; Sparks et al, 2012). Such tools can provide a reproducible, transparent, documented framework that reinforces objective operational forecasting procedures and guidance, and can increase the level of public trust in volcanologists’ advice (Barclay et al, 2015). The focus of such tools is forecasting when and how (e.g., hazard footprint/severity, eruption duration) an imminent eruption will occur
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
