Developing an Event Tree for probabilistic hazard and risk assessment at Vesuvius
Augusto NeriWilly AspinallRaffaello CioniA. BertagniniPeter J. BaxterGiulio ZuccaroDaniele AndronicoSara BarsottiPaul ColeTomaso Esposti OngaroThea HincksGiovanni MacedonioPaolo PapaleMauro RosiRoberto SantacroceGordon Woo
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Keywords:
Event tree
Volcanic hazards
Hazard map
Tree (set theory)
In the case of the volcanic eruption of Mount Usu in 2000, there were no victims because the evacuation activities were carried out smoothly, as people made great use of the hazard map of Usu Volcano as a source of information. Also, the Mount Fuji Hazard Map Committee started work in 2001, scheduled to publicize the results in the near future. Although volcanic hazard maps have only recently captured the attention to people in Japan, they have been used in various overseas countries for many years. This paper summarizes the definition of volcanic hazard maps and the method of making volcanic hazard maps, classifies and analyzes the collected foreign cases, and introduces representative examples.
Hazard map
Mount
Volcanic hazards
Geologic hazards
Vulcanian eruption
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Distribution of landslide is controlled by various causative factors that have different impacts on the occurrence of landslide in different regions. Using one single model to build the hazard assessment is not enough to fully reflect the spatial differences of landslide controlling factors especially for large area. Landslide hazard assessment based on zonation was therefore proposed in this study with an attempt to take effective measures to address this problem. The China–Pakistan Economic Corridor was taken as the study area where landslide hazard assessment was carried out. Based on the features of geological structure, topography, and climate, the study area was divided into three zones. The controlling factors were further analyzed by the geographical detectors method. It was found that the main controlling factors for landslides in these three zones were related to the site’s topography (altitude, slope gradient, and relief amplitude), land use, and distance to an earthquake epicenter. Furthermore, different factors for landslide hazard assessment were selected based on the result of a controlling factor analysis. An artificial neural network model was employed to build the hazard assessment models, and hazard assessment maps were generated. Validations were conducted, showing that the accuracy of hazard assessment maps by zones was higher than that by the whole study area, despite there was no significant difference during the modeling process.
Hazard map
Epicenter
Natural hazard
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In this paper, we explore the effects of the intrinsic uncertainties upon long-term volcanic hazard by analyzing tephra fall hazard at Campi Flegrei, Italy, using the BET_VH model described in Marzocchi et al. (Bull Volcanol, 2010). The results obtained show that volcanic hazard based on the weighted average of all possible eruptive settings (i.e. size classes and vent locations) is significantly different from an analysis based on a single reference setting, as commonly used in volcanic hazard practice. The long-term hazard map for tephra fall at Campi Flegrei obtained here accounts for a wide spectrum of uncertainties which are usually neglected, largely reducing the bias intrinsically introduced by the choice of a specific reference setting. We formally develop and apply a general method to recursively integrate simulations from different models which have different characteristics in terms of spatial coverage, resolution and physical details. This outcome of simulations will be eventually merged with field data through the use of the BET_VH model.
Volcanic hazards
Hazard map
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