Bayesian Hierarchical Time Predictable Model for eruption occurrence: an application to Kilauea Volcano

The physical processes responsible for volcanic eruptions are characterized by a large number
of degrees of freedom, often non-linearly coupled. This extreme complexity leads to an
intrinsic deterministic unpredictability of such events that can be satisfactorily described by
a stochastic process. Here, we address the long-term eruption forecasting of open conduit
volcanoes through a Bayesian Hierarchical Modelling information in the catalogue of past
eruptions, such as the time of occurrence, the duration, and the erupted volumes. The aim of
the model is twofold: (1) to get new insight about the physics of the process, using the model
to test some basic physical hypotheses of the eruptive process and (2) to build a stochastic
model for long-term eruption forecasting; this is the basic component of Probabilistic Volcanic
Hazard Assessment that is used for rational land use planning and to design Emergency plan.
We apply the model to Kilauea eruption occurrences and check its feasibility to be included
in Probabilistic Volcanic Hazard Assessment.