Representative Natural Hazard Scenarios for Risk Assessment of Spatially Distributed Infrastructure Systems

Rosero‐Velásquez, Hugo; Straub, Daniel

doi:10.3850/978-981-11-2724-3_0821-cd

Cited by 7 publications

(4 citation statements)

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“…Moreover, this approach makes it possible to illustrate and to assess the complexity of the different interconnections and the related first, second and further order effects using a mixed dialogue and model-based approach. Existing approaches are mostly limited to a single sector [75] or a local scale [60] and do not include experimental knowledge and expertise of local stakeholders. Most important is the fact that our participatory and heuristic-driven approach made it possible to cope with the multi-dimensional complexity of interdependencies and interrelations between the sectors, as well as between actor networks.…”

Section: Discussionmentioning

confidence: 99%

Participatory Assessment of Multi Risks in Urban Regions—The Case of Critical Infrastructures in Metropolitan Lima

et al. 2021

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This paper aims to provide a comprehensive picture of risk governance and conceptualize an approach to dealing with multi-risks in the Metropolitan Region of Lima, Peru. We argue that the impacts of extreme events are not solely determined by a given place-based vulnerability and risk profile but are considerably influenced by cascading effects caused by service disruptions of critical infrastructures, which may even take place outside the exposed areas. This paper tests a new conceptual framework for assessing criticality and provides an evidence basis for effective risk governance of critical infrastructures in urban regions. The findings are based on a multi-method approach which includes participatory activities. The overall results show that the electricity sector is the sector with the highest systemic criticality, followed by IT and emergency response. These results help to identify gaps in actors’ awareness of interdependencies and show the general criticalities of infrastructures with regard to both physical and actor-related factors. A better understanding of the given interconnection between sectors, but also of specific system elements, is an indispensable prerequisite for resilience building. Furthermore, the analysis underlines specific cooperation and communication needs between different stakeholders but also indicates the requirement for a prioritization of sectors in contingency plans and spatial planning.

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Section: Discussionmentioning

confidence: 99%

Participatory Assessment of Multi Risks in Urban Regions—The Case of Critical Infrastructures in Metropolitan Lima

et al. 2021

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“…The iteratively constructed RIESGOS demonstrator for a multi-risk information system is based on a modular and scalable concept in which the different hazards, the related exposure models, and vulnerability schemas are each represented by one individual web service. These independent and distributed web-services (managed and maintained by individual research institutions) are based on the quantitative methodologies developed within the RIESGOS framework for multi-risk analysis (i.e., [69,[89][90][91][92][93][94]). Therefore, their integration into the RIESGOS demonstrator simulates the multi-risk environment of Latacunga.…”

Section: The Riesgos Demonstrator Tool For Quantitative Multi-risk Anmentioning

confidence: 99%

Community Perception and Communication of Volcanic Risk from the Cotopaxi Volcano in Latacunga, Ecuador

Zapata

Parrado²,

Frimberger

et al. 2021

Sustainability

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The inhabitants of Latacunga living in the surrounding of the Cotopaxi volcano (Ecuador) are exposed to several hazards and related disasters. After the last 2015 volcanic eruption, it became evident once again how important it is for the exposed population to understand their own social, physical, and systemic vulnerability. Effective risk communication is essential before the occurrence of a volcanic crisis. This study integrates quantitative risk and semi-quantitative social risk perceptions, aiming for risk-informed communities. We present the use of the RIESGOS demonstrator for interactive exploration and visualisation of risk scenarios. The development of this demonstrator through an iterative process with the local experts and potential end-users increases both the quality of the technical tool as well as its practical applicability. Moreover, the community risk perception in a focused area was investigated through online and field surveys. Geo-located interviews are used to map the social perception of volcanic risk factors. Scenario-based outcomes from quantitative risk assessment obtained by the RIESGOS demonstrator are compared with the semi-quantitative risk perceptions. We have found that further efforts are required to provide the exposed communities with a better understanding of the concepts of hazard scenario and intensity.

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“…In this example, we consider the IEEE39 benchmark system, a simplified model of the high voltage transmission system in the northeast of the U.S.A. The model was first presented in 1970 [44] and has been extensively used as a benchmark model in power system analysis [45][46][47].…”

Section: Power Transmission Network With Cascading Failurementioning

confidence: 99%

Bayesian Cross Entropy Method for Network Reliability Assessment

Chan¹,

Papaioannou²,

Straub³

2021

Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)

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We propose a modification of the improved cross entropy (iCE) method to enhance its performance for network reliability assessment. The iCE method performs a transition from the nominal density to the optimal importance sampling (IS) density via a parametric distribution model whose cross entropy with the optimal IS is minimized. The efficiency and accuracy of the iCE method are largely influenced by the choice of the parametric model. In the context of reliability of systems with independent multi-state components, the obvious choice of the parametric family is the categorical distribution. When updating this distribution model with standard iCE, the probability assigned to a certain category often converges to 0 due to lack of occurrence of samples from this category during the adaptive sampling process, resulting in a poor IS estima tor with a strong negative bias. To circumvent this issue, we propose an algorithm termed Bayesian improved cross entropy method (BiCE). Thereby, the posterior predictive distribution is employed to update the parametric model instead of the weighted maximum likelihood estimation approach employed in the original iCE method. A set of numerical examples illustrate the efficiency and accuracy of the proposed method.

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Representative Natural Hazard Scenarios for Risk Assessment of Spatially Distributed Infrastructure Systems

Cited by 7 publications

References 0 publications

Participatory Assessment of Multi Risks in Urban Regions—The Case of Critical Infrastructures in Metropolitan Lima

Participatory Assessment of Multi Risks in Urban Regions—The Case of Critical Infrastructures in Metropolitan Lima

Community Perception and Communication of Volcanic Risk from the Cotopaxi Volcano in Latacunga, Ecuador

Bayesian Cross Entropy Method for Network Reliability Assessment

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