How big will the next eruption be?

Abstract

Abstract Anticipating the size of the next volcanic eruption in long-term forecasts is a major problem in both basic and applied volcanology. In this study, we investigate the extent to which eruption size is predictable based on historical and other attribute data. Data from the Smithsonian Global Volcanism Program (GVP) Catalog is used to determine the predictability of volcanic eruption size as quantified through the reported VEI (Volcano Explosivity Index). The numerical and categorical attributes from the global volcanic catalog were classified with trained random forest and simple prediction models to make a forecast of VEI that can be tested against the most recent eruption of each volcano. We compare these results to two different baseline predictability levels by: (a) selecting randomly from the global distribution of VEIs for the most recent eruptions to calculate a cohort baseline and (b) selecting the most frequent VEI for a given population to calculate a zero-rule baseline. We found that: (1) nearly any method that incorporates prior information on a specific volcano improves the prediction accuracy of the succeeding eruption VEI by at least 10 percentage points relative to the cohort baseline case, (2) incorporating attributes beyond previous VEIs can provide better accuracy and achieve up to 30 percentage point accuracy gains, (3) total accuracy of the VEI forecasting by these methods can be up to nearly 80% and (4) the zero-rule is an effective prediction method that is modestly outperformed (~ 5 percentage point gain) by random forest methods with multiple attributes on most datasets. We find no notable preference in accuracy based on volcano type. The results quantify the importance of volcano-specific information in long-term forecasting and may help practitioners assess their expected performance when anticipating future eruption size.

Keywords

• VEI
• Forecasting
• Machine learning