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.

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

Cannata, Andrea
Secondo
;
Cassisi, Carmelo;Prestifilippo, Michele;GAMBINO, SALVATORE
2017-01-01

Abstract

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.
2017
Bayesian networks; early warning system; multivariate model; probabilistic model; volcano monitoring; Geophysics; Geochemistry and Petrology; Earth and Planetary Sciences (miscellaneous); Space and Planetary Science
File in questo prodotto:
File Dimensione Formato  
43_Cannavo_et_al., 2017 JGR.pdf

solo gestori archivio

Tipologia: Versione Editoriale (PDF)
Dimensione 2.75 MB
Formato Adobe PDF
2.75 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/363233
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 25
  • ???jsp.display-item.citation.isi??? 22
social impact