A simulation and machine learning approach to critical infrastructure resilience appraisal : Case study on payment disruptions
Abstract: This study uses a simulation to gather data regarding a payment disruption. The simulation is part of a project called CCRAAAFFFTING, which examines what happens to a society when a payment disruption occurs. The purpose of this study is to develop a measure for resilience in the simulation and use machine learning to analyse the attributes in the simulation to see how they affect the resilience in the society. The resilience is defined as “the ability to bounce back to a previous state”, and the resilience measure is developed according to this definition. Two resilience measurements are defined, one which relates the simulated value to the best-case and worst-case scenarios, and the other which takes the pace of change in values into consideration. These two measurements are then combined to one measure of the total resilience. The three machine learning algorithms compared are Neural Network, Support Vector Machine and Random Forest, and the performance measure of these are the error rate. The results show that Random Forest performs significantly better than the other two algorithms, and that the most important attributes in the simulation are those concerning the customers’ ability to make purchases in the simulation. The developed resilience measure proves to respond logically to how the situation unfolded, and some suggestions to further improve the measurement is provided for future research.
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