Eruption Forecasting using Bayesian Networks

What is a Bayesian Network?

A Bayesian Network is a simple model and statistical tool that can be used to determine the probability of an event happening, based on:

• a set of variables (nodes) that influence the event occurring,
• the relationships and dependencies between the variables,
• prior information about the event, and
• prior information about the variables.

Bayesian networks can combine different sources of information by defining a system of conditional probabilities between sources. An event is then simply a certain constellation of that system for which the Bayesian Network will give us a probability of occurrence.

A Bayesian network (e.g. see Figure 1) has two main components:

1. the causal relationships between the variables in the system, and
2. the actual probabilities for the variable (node) that are used to make predictions.

A Bayesian network is learned from data and previous patterns. Having knowledge of a previous event can help the model to predict the probability of a future event. Because Bayesian Networks also include prior information, they  are resilient to gaps in information (such as missing sensors).

Eruption Forecasting at Whakaari | White Island

Since the 2019 eruption, destruction of monitoring equipment on island means that Whakaari (White Island) has reduced real-time monitoring data. Yet, eruption forecasting is still possible through the ongoing monitoring of volcanic activity (e.g. remote sensing, monthly gas plume monitoring). Using Bayesian Networks, we have developed and tested a novel method of eruption forecasting that does not solely rely on on-island data streams.

The Bayesian Network was trained on most of the Whakaari monitoring data available since 2009. Figure 2 shows the timing of past eruptions on Whakaari. Group A data (2009-2012) was the initial training set used to model the 2013 eruption, Group A & B were used to predict the 2016 eruption, and so forth. In this manner, the error margins of the network were greatly improved, and the model was found to be effective even when not all data streams are available, such as the current day situation. Results from this network showed increases in eruption likelihood prior to the 2012-2019 sequences, even when using airborne gas measurements only.

Once this new method has gone through the scientific peer-review process, it will become an important part in setting life safety parameters around the volcano. Forecasts will be updated automatically as new monitoring data becomes available and provided to geohazard response staff. This information will help GNS Science to perform it’s monitoring and advisory requirements to advise external stakeholders of increases in eruption likelihood.

Forecasts from our BN are easy to understand and interpret by volcanologists, which adds to its usefulness for decision support and expert advice. Expert judgment can also be easily integrated and potentially help to improve forecasts in the absence of data.