Sources, Nature, and Impact of Uncertainties on Catastrophe Modeling

Grossi, Patricia; Windeler, Don

doi:10.1007/0-387-23129-3_4

Cited by 3 publications

(6 citation statements)

References 5 publications

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“…In general, different sources of uncertainty associate the problem identification and risk analysis phases in the risk assessment methodology. These uncertainties might be related to the system variability and randomness, the presence of errors, either in the measurements, or modeling and analysis, scenarios or data insignificance, and lack of knowledge, indeterminacy, judgment, and linguistic imprecision in decision making [17][18][19][20][21][22][23][24][25]. Some of these uncertainties could be reduced and others are irreducible.…”

Section: Uncertainty Classificationmentioning

confidence: 99%

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Introductory Chapter: Uncertainty Management to Support Pollution Prevention and Control Decisions

Rahman¹,

Hung²

2022

Engineering Problems - Uncertainties, Constraints and Optimization Techniques

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Section: Uncertainty Classificationmentioning

confidence: 99%

“…Two uncertainty classification systems are used; the first is based on the ability to reduce these uncertainties and the second is based on their sources [18][19][20][21][22][23][24]. The first consists of two classes, i.e.…”

Section: Uncertainty Classificationmentioning

confidence: 99%

Introductory Chapter: Uncertainty Management to Support Pollution Prevention and Control Decisions

Rahman¹,

Hung²

2022

Engineering Problems - Uncertainties, Constraints and Optimization Techniques

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“…When modeling earthquake risk, one must be aware of the large amount of uncertainties related mainly to the hazard and to the vulnerability (Ordaz 2000;Grossi 2004;Murphy et al 2011;Marulanda et al 2013;Salgado-Gálvez et al 2014b;Salgado-Gálvez et al 2015a). …”

Section: Probabilistic Seismic Risk Assessmentmentioning

confidence: 99%

Probabilistic estimation of annual lost economic production due to premature deaths because of earthquakes

Salgado‐Gálvez

Bernal

Barbat

et al. 2015

Human and Ecological Risk Assessment: An International Journal

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This is an Accepted Manuscript of an article published by Taylor & Francis Group in Human and Ecological Risk Assessment: An International Journal on 2016, available online at: http://www.tandfonline.com/10.1080/10807039.2015.1095072A methodology to estimate, in a probabilistic way, the annual cost to society of premature deaths because of earthquakes is proposed in this article. The methodology makes use of results obtained by means of prospective and probabilistic seismic risk assessments where expected deaths caused by the collapse of buildings are obtained. Those results, combined with demographic and macroeconomic indicators such as the age distribution, life expectancy at birth, and per capita gross domestic product, are used to estimate the cost to society in terms of lost productivity due to premature mortality because of earthquakes. The proposed methodology does not attempt to estimate nor assign a cost to human lives at any stage, but the one associated to lost productivity at the societal level. One of the descriptors of the methodology is part of the components of the disability adjusted life year, a widely used metric in the public health field that estimates the burden of diseases based mostly on historical data. As an example, the methodology is applied to Medellin, the second largest city of Colombia, finding that the cost of lost productivity due to premature mortality because of earthquakes has a similar order of magnitude to the direct physical losses in the public and private building stock calculated in a previous probabilistic seismic risk assessment.Peer ReviewedPostprint (author's final draft

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“…A greater concern in quantifying uncertainty is the deficiency of both quantitative and qualitative data for describing the four components-inventory, hazards, loss, and vulnerability-in a catastrophe model (Grossi, 2004). For example, seismological data describing earthquake occurrence in the Caribbean are limited to only a few decades, which makes the updating of the recurrence distributions problematic (Young, 2008).…”

Section: The Uncertainties Of the Catastrophe Modelsmentioning

confidence: 99%

“…Another source of epistemic uncertainty in a catastrophe model is the paucity of accessible data to generate the geographic information system (GIS) databases within the modelling software (Grossi, 2004;Grossi and Kunreuther, 2005). For any model, recognition of the importance of input data is essential.…”

Section: The Uncertainties Of the Catastrophe Modelsmentioning

confidence: 99%

Disaster risk insurance and catastrophe models in risk‐prone small Caribbean islands

2014

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Post-catastrophe recovery and financial liquidity have long challenged small Caribbean islands. These states are vulnerable to multifarious natural hazards that often cause considerable socioeconomic dislocation. Such events inflict heavy losses on businesses and households, and significantly disrupt all aspects of government operations. After Hurricane Ivan devastated the economies of some islands in September 2004-with estimated losses of as much as 200 per cent of gross domestic product in some cases-regional governments, aided by the World Bank and international donors, approved the creation of a regional catastrophe insurance scheme. This parametric-based mechanism is underpinned by derivatives-based catastrophe modelling whose outputs determine policy triggers and pay outs. Hazard models, particularly catastrophe models, are not widely accepted as yet. Despite recent advancements, major concerns have rendered them peripheral tools for many establishments. This paper reviews the region's vulnerabilities and examines constraints on the application of these models and suggests a means of improving their efficacy and acceptability.

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Sources, Nature, and Impact of Uncertainties on Catastrophe Modeling

Cited by 3 publications

References 5 publications

Introductory Chapter: Uncertainty Management to Support Pollution Prevention and Control Decisions

Introductory Chapter: Uncertainty Management to Support Pollution Prevention and Control Decisions

Probabilistic estimation of annual lost economic production due to premature deaths because of earthquakes

Disaster risk insurance and catastrophe models in risk‐prone small Caribbean islands

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