聚类算法在基础设施弹性预测模型中的应用
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models
摘要：
最近的研究越来越多地采用基于仿真的机器学习（ML）模型来分析关键基础设施系统的弹性。对于实际应用，这些ML模型考虑了在紧急情况下影响网络响应的组件级特征。然而，这种方法可能会产生大量的特征，并导致ML模型遭受“维数灾难”。我们提出了一种基于聚类的方法，该方法同时最大限度地减少了高维问题，并提高了为大规模相互依赖的基础设施网络中的弹性分析而开发的ML模型的预测准确性。该方法包括三个部分：（a）模拟数据集的生成，（b）网络组件聚类，以及（c）预测模型的降维和开发。首先，一个相互依赖的基础设施模拟模型模拟各种破坏性事件的网络范围的后果。从模拟数据中提取组件级特征。接下来，聚类算法被用来获得集群级别的功能，通过分组组件级功能的基础上，他们的拓扑和功能特性。最后，ML算法用于开发模型，使用集群级特征预测破坏性事件的网络范围影响。该方法的适用性证明使用一个相互依赖的电力水运输试验台。所提出的方法可用于开发基础设施网络灾后恢复的决策支持工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
