Using hidden multi-state Markov models with multi-parameter volcanic data to provide empirical evidence for alert level decision-support

Aspinall, Willy; Carniel, Roberto; Jaquet, Olivier; Woo, Gordon; Hincks, Thea

doi:10.1016/j.jvolgeores.2005.08.010

Cited by 55 publications

(30 citation statements)

References 27 publications

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“…Bayesian approaches are often used in conjunction with Event Trees (e.g., Newhall and Hoblitt 2002;Marzocchi et al 2004Marzocchi et al , 2008Newhall and Pallister 2015, and references therein), that represent the complex ramification of possible outcomes, each one quantified as a probability distribution which is allowed to evolve as long as new information is added (e.g., when new observations are available). To-date, Bayesian approaches have been employed in a large number of situations in volcanology, including forecasts of volcanic hazards over the shortterm (Aspinall et al 2003(Aspinall et al , 2006Marzocchi et al 2008; Lindsay et al 2010;Brancato et al 2011Brancato et al , 2012Bell and Kilburn 2012;Marzocchi and Bebbington 2012;Sandri et al 2009Sandri et al , 2012Selva et al 2012Selva et al , 2014Garcia-Aristizabal et al 2013;Anderson and Segall 2013;Rouwet et al 2014;Aspinall and Woo 2014;Sobradelo et al 2015;Boue et al 2015;Tonini et al 2016;Bartolini et al 2016) as well as over the long-term (Martin et al 2004;Baxter et al 2008;Neri et al 2008;Orsi et al 2009;Marzocchi et al 2008Marzocchi et al , 2010Sobradelo and Martì 2010;Passarelli et al 2010a, b;Selva et al 2012;Marzocchi and Bebbington 2012;Sandri et al 2012Sandri et al , 201...…”

Section: Rational Volcanic Hazard Forecastsmentioning

confidence: 99%

Rational volcanic hazard forecasts and the use of volcanic alert levels

Papale

2017

J Appl. Volcanol.

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Volcanologists make hazard forecasts in order to contribute to volcanic risk assessments and decision-making, in areas where volcanic phenomena have the potential to impact societal assets. Present-day forecasts related to the potential occurrence of an eruption mostly take the form of alert levels, that are established by volcano scientists with the aim of communicating the state of a volcano and its possible short-term evolution. Here I analyse current alert level systems and their role in decision-making processes. I show that the use of such systems implies predictive capabilities not supported by corresponding levels of confidence in the knowledge of the volcano. Their use also implies an assumption of volcano scientist responsibility for decisions that goes beyond the expertise of the scientist, and which, in most countries, is not granted by a corresponding societal mandate. A rational volcanic hazard forecast system accepts instead the uncertain nature of volcanic processes and the consequent limited predictive capabilities; forecasts expressed as probabilities, or better as probability distributions, reflect a rational attitude by scientists and assure clear roles that reflect both expertise and societal mandate to any group involved in the management of a volcanic crisis. Exceptions where the use of volcanic alert levels may lead to efficient management are also discussed.

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Section: Rational Volcanic Hazard Forecastsmentioning

confidence: 99%

Rational volcanic hazard forecasts and the use of volcanic alert levels

Papale

2017

J Appl. Volcanol.

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“…Some of the earliest, (Wickman, 1965(Wickman, , 1976Reyment, 1969;Klein, 1982) employed stochastic principles to analyze eruption patterns. Further studies included transition probabilities of Markov chains (Carta et al, 1981;Aspinall et al, 2006;Bebbington, 2007), Bayesian analysis of volcanic activity (Ho, 1990;Solow, 2001;Newhall and Hoblitt, 2002;Ho et al, 2006;Marzocchi et al, 2008), homogeneous and non-homogeneous Poisson process applied to volcanic series (De la Cruz-Reyna, 1991;426 A. T. Mendoza-Rosas and S. De la Cruz-Reyna: A simple and precise assessment of the volcanic hazard 1996a, b), geostatistical hazard-estimation methods (Jaquet et al, 2000;Jaquet and Carniel, 2006), a mixture of Weibull distributions (Turner et al, 2008) and non-homogeneous statistics to link geological and historical eruption time series (Mendoza-Rosas and De la ). An exhaustive list of the available literature on this subject is beyond the scope of this paper, and the above references only attempt to illustrate the diversity of methods that have been applied to the volcanic eruption sequences.…”

Section: Introductionmentioning

confidence: 99%

A mixture of exponentials distribution for a simple and precise assessment of the volcanic hazard

Mendoza-Rosas¹,

Cruz‐Reyna²

2009

Nat. Hazards Earth Syst. Sci.

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Abstract. The assessment of volcanic hazard is the first step for disaster mitigation. The distribution of repose periods between eruptions provides important information about the probability of new eruptions occurring within given time intervals. The quality of the probability estimate, i.e., of the hazard assessment, depends on the capacity of the chosen statistical model to describe the actual distribution of the repose times. In this work, we use a mixture of exponentials distribution, namely the sum of exponential distributions characterized by the different eruption occurrence rates that may be recognized inspecting the cumulative number of eruptions with time in specific VEI (Volcanic Explosivity Index) categories. The most striking property of an exponential mixture density is that the shape of the density function is flexible in a way similar to the frequently used Weibull distribution, matching long-tailed distributions and allowing clustering and time dependence of the eruption sequence, with distribution parameters that can be readily obtained from the observed occurrence rates. Thus, the mixture of exponentials turns out to be more precise and much easier to apply than the Weibull distribution. We recommended the use of a mixture of exponentials distribution when regimes with well-defined eruption rates can be identified in the cumulative series of events. As an example, we apply the mixture of exponential distributions to the repose-time sequences between explosive eruptions of the Colima and Popocatépetl volcanoes, México, and compare the results obtained with the Weibull and other distributions.

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“…After previous deterministic attempts, forecasting is now being made mainly using a probabilistic approach, also including Bayesian event tree models, which recognize natural variability and stochastic elements, include the full range of possible events and show most likely scenarios (Sparks, 2003;Aspinall et al, 2006;Marzocchi et al, 2008). Forecasting should not be restricted at evaluating only the probability of occurrence of an impeding eruption, but also its expected location, size and style, including the occurrence of ash plumes, which may affect areas very distant from the volcano.…”

Section: Challenge 5: Eruption Forecastingmentioning

confidence: 99%

Great challenges in volcanology: how does the volcano factory work?

Acocella

2014

Front. Earth Sci.

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Using hidden multi-state Markov models with multi-parameter volcanic data to provide empirical evidence for alert level decision-support

Cited by 55 publications

References 27 publications

Rational volcanic hazard forecasts and the use of volcanic alert levels

Rational volcanic hazard forecasts and the use of volcanic alert levels

A mixture of exponentials distribution for a simple and precise assessment of the volcanic hazard

Great challenges in volcanology: how does the volcano factory work?

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