Abstract
In the face of the budget cuts and increased size of industry infrastructure, one of the top priorities for industry infrastructure protection is to identify critical regions by vulnerability analysis. Then, limited resources can be allocated to those critical regions. Unfortunately, difficulties can be observed in existing approaches of vulnerability analysis. Some of them are unavailable due to the insufficient data. Others are susceptible to human biases. Here, we propose an approach to overcome these difficulties based on the location data of failure events. The critical geographic regions are determined by the risk ranking of different candidate regions. Risk is calculated by integrating the probability of the failure event occurring (risk uncertainty) and total failure cost (the severity of failure consequences) in each candidate region. By changing the modeled object from the components to the region where the whole industry infrastructure is located, it collects the rarely failure events which are dispersed in different positions of the industry infrastructure to provide sufficient data, then the probability can be obtained by using a Poisson point process and kernel density estimation. Meanwhile, the application of hypothesis testing avoids the susceptibility of the approach to human biases by verifying the correctness of the assumptions used in the approach. Finally, a case study of this approach is performed on a pipeline network in Kansas, USA. In addition to the validation of the feasibility of our approach, risk uncertainty is proven to be less instructive for identifying critical regions than the severity of failure consequences.
Highlights
The stable, continuous and safe operation of critical industry infrastructure is significantly affected by different types of hazards
POISSON POINT PROCESS As a widely used spatial statistical technique, a Poisson point process N can model the data of spatial points
This paper proposes an improved empirical approach for the vulnerability analysis of industry infrastructure to identify critical regions
Summary
The stable, continuous and safe operation of critical industry infrastructure is significantly affected by different types of hazards. These hazards have caused many failure events in critical industry infrastructures. Due to these failure events, the provision of industry infrastructure services is disrupted. The stability of the economy or national defense will be seriously affected. Critical industry infrastructure protection has attained great importance in different countries [1]–[4]. As an important part of industry infrastructure protection, the identification of critical regions is to determine the regions
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