Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models

• URL: http://arxiv.org/abs/2205.03316v1
• Date: Fri, 6 May 2022 15:51:05 GMT
• Title: Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models
• Authors: Srijith Balakrishnan, Beatrice Cassottana, Arun Verma
• Abstract summary: We present a clustering-based method that simultaneously minimizes the problem of high-dimensionality and improves the prediction accuracy of machine learning models. The proposed method can be used to develop decision-support tools for post-disaster recovery of infrastructure networks.
• Score: 4.350783459690612
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent studies increasingly adopt simulation-based machine learning (ML) models to analyze critical infrastructure system resilience. For realistic applications, these ML models consider the component-level characteristics that influence the network response during emergencies. However, such an approach could result in a large number of features and cause ML models to suffer from the `curse of dimensionality'. We present a clustering-based method that simultaneously minimizes the problem of high-dimensionality and improves the prediction accuracy of ML models developed for resilience analysis in large-scale interdependent infrastructure networks. The methodology has three parts: (a) generation of simulation dataset, (b) network component clustering, and (c) dimensionality reduction and development of prediction models. First, an interdependent infrastructure simulation model simulates the network-wide consequences of various disruptive events. The component-level features are extracted from the simulated data. Next, clustering algorithms are used to derive the cluster-level features by grouping component-level features based on their topological and functional characteristics. Finally, ML algorithms are used to develop models that predict the network-wide impacts of disruptive events using the cluster-level features. The applicability of the method is demonstrated using an interdependent power-water-transport testbed. The proposed method can be used to develop decision-support tools for post-disaster recovery of infrastructure networks.

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