Vulnerable Systems

Kröger, Wolfgang; Zio, Enrico

doi:10.1007/978-0-85729-655-9

Cited by 169 publications

(148 citation statements)

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“…Consequently, the ontology on vulnerable systems shown in Fig. 3 explicitly refers to four classes of vulnerable systems: (1) ''natural systems'' for vulnerability studies referring to a set of subclasses that include physical systems (Calvalieri et al 2012), biological systems (De Lange et al 2010), and/or biophysical systems (O'Brien et al 2004); (2) ''social systems'' for vulnerability studies referring to the subclasses of population in general (Adger 1999;Carreño et al 2007), social groups, for example, communities (Cutter et al 2003;Bollin and Hidajat 2006), functional systems, such as the economy (Patt et al 2010), the public financial sector (Mechler et al 2006) or the health sector (Hahn et al 2009;Few and Tran 2010); and (3) ''technical systems,'' such as vulnerability studies referring to critical infrastructure (Hellström 2007;Kröger and Zio 2011). In addition, the ontology also accounts for a separate class of hybrid concepts referring to interactions between and within systems, such as in societal and ecological (biophysical) subsystems (Turner et al 2003;Gallopín 2006) or societal and technical subsystems (Khazai et al 2013).…”

Section: Vulnerable Systems-vulnerability Of What?mentioning

confidence: 99%

VuWiki: An Ontology-Based Semantic Wiki for Vulnerability Assessments

Khazai

Kunz-Plapp

Büscher

et al. 2014

Int J Disaster Risk Sci

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Three decades of vulnerability research have generated a complex and growing body of knowledge. The concept of vulnerability, as well as its implementation in vulnerability assessments, is used in various disciplines and contexts. Correspondingly, a multitude of ideas and frameworks about how to conceptualize and measure vulnerability exists. To provide a structured representation of vulnerability, we have set up an ontology-based semantic wiki for reviewing and representing vulnerability assessments (www.vuwiki.org). Based on a survey of 55 vulnerability assessment studies, we first developed an ontology as an explicit reference system for describing vulnerability assessments. The ontology was then implemented in a semantic wiki which allows for the classification and annotation of vulnerability assessment. The resulting semantic wiki, VuWiki, does not aim at ''synthesizing'' a holistic and overarching model of vulnerability, but at (1) providing-both scientists and practitioners-with a uniform ontology as a reference system; (2) providing easy and structured access to the knowledge field of vulnerability assessments with the possibility for any user to retrieve assessments using specific research criteria; and (3) serving as a collaborative knowledge platform that allows for the active participation of those generating and using the knowledge represented in the vulnerability wiki.

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Section: Vulnerable Systems-vulnerability Of What?mentioning

confidence: 99%

VuWiki: An Ontology-Based Semantic Wiki for Vulnerability Assessments

Khazai

Kunz-Plapp

Büscher

et al. 2014

Int J Disaster Risk Sci

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“…For example, unexpected events occurring in critical infrastructures, such as power distribution networks, can propagate their disrupting consequences to other connected systems and lead to cascading effects with huge economical and safety impacts (Kröger & Zio, 2011;Vaiman et al, 2012;Zio, 2016a). However, such consequences can be strongly mitigated if those events are -to some extent -known in advance (PateCornell, 2002;Paté-Cornell, 2012).…”

Section: Introductionmentioning

confidence: 99%

Simulation-based exploration of high-dimensional system models for identifying unexpected events

Turati

Pedroni

Zio

2017

Reliability Engineering & System Safety

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. Simulation-based exploration of highdimensional system models for identifying unexpected events. Reliability Engineering and System Safety.http://dx.doi.org/10.1016/j.ress.2017.04.004Simulation-based exploration of high-dimensional system models for identifying unexpected events AbstractMathematical numerical models are increasingly employed to simulate system behavior and identify sequences of events or configurations of the system's design and operational parameters that can lead the system to extreme conditions (Critical Region, CR). However, when a numerical model is: i) computationally expensive, ii) high-dimensional, and iii) complex, these tasks become challenging.In this paper, we propose an adaptive framework for efficiently tackling this problem: i) a dimensionality reduction technique is employed for identifying the factors and variables that most affect the system behavior; ii) a meta-model is sequentially trained to replace the computationally expensive model with a computationally cheap one; iii) an adaptive exploration algorithm based on Markov Chain Monte Carlo is introduced for exploring the system state space using the meta-model; iv) clustering and other techniques for the visualization of high dimensional data (e.g., parallel coordinates plot) are employed to summarize the retrieved information.The method is employed to explore a power network model involving 20 inputs. The CRs are properly identified with a limited computational cost, compared to another exploration technique of literature (i.e., Latin Hypercube Sampling).

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“…A good estimation of the type of failure requires a good estimation of how the failure of part of the network can spread, which is something that is dependent on the network topology and requires appropriate consideration of * hackl@ibi.baug.ethz.ch the mutual interplay between the structural complexity and functional dynamics of the network [4]. The estimation of the type of network failure is usually made using the exact network topology.…”

Section: Introductionmentioning

confidence: 99%

Generation of Spatially Embedded Random Networks to Model Complex Transportation Networks

Hackl

Adey

2016

14th International Probabilistic Workshop

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Random networks are increasingly used to analyse complex transportation networks, such as airline routes, roads and rail networks. So far, this research has been focused on describing the properties of the networks with the help of random networks, often without considering their spatial properties. In this article, a methodology is proposed to create random networks conserving their spatial properties. The produced random networks are not intended to be an accurate model of the real-world network being investigated, but are to be used to gain insight into the functioning of the network taking into consideration its spatial properties, which has potential to be useful in many types of analysis, e.g. estimating the network related risk. The proposed methodology combines a spatial non-homogeneous point process for vertex creation, which accounts for the spatial distribution of vertices, considering clustering effects of the network and a hybrid connection model for the edge creation. To illustrate the ability of the proposed methodology to be used to gain insight into a real world network, it is used to estimate standard structural statistics for part of the Swiss road network, and these are then compared with the known values.

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Vulnerable Systems

Cited by 169 publications

References 0 publications

VuWiki: An Ontology-Based Semantic Wiki for Vulnerability Assessments

VuWiki: An Ontology-Based Semantic Wiki for Vulnerability Assessments

Simulation-based exploration of high-dimensional system models for identifying unexpected events

Generation of Spatially Embedded Random Networks to Model Complex Transportation Networks

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