A multivariate probabilistic graphical model for real-time volcano monitoring on Mount Etna

Real-time assessment of the state of a volcano plays a key role for civil protection purposes.
Unfortunately, because of the coupling of highly nonlinear and partially known complex volcanic processes,
and the intrinsic uncertainties in measured parameters, the state of a volcano needs to be expressed in
probabilistic terms, thus making any rapid assessment sometimes impractical. With the aim of aiding
on-duty personnel in volcano-monitoring roles, we present an expert system approach to automatically
estimate the ongoing state of a volcano from all available measurements. The system consists of a
probabilistic model that encodes the conditional dependencies between measurements and volcanic states
in a directed acyclic graph and renders an estimation of the probability distribution of the feasible volcanic
states.We test the model with Mount Etna (Italy) as a case study by considering a long record of multivariate
data. Results indicate that the proposed model is effective for early warning and has considerable potential
for decision-making purposes.