Machine learning approaches to modeling interdependent network restoration time

The recovery of an infrastructure system after a disruptive event is vital for other systems (and for the community) that require its functionality. Disruptive events occur due to various reasons and for a system to be resilient, it is important to be prepared and ready to respond and restore. Understanding the time required for restoration for different disruptive scenarios enables decision-makers to plan for and schedule resources. In this research work, we explore different machine learning techniques to predict the time taken for an interdependent network to be restored after a disruption. We use as independent variables the restoration rates of disrupted components, and we generate the resulting network restoration time dependent variable from a network restoration optimization model. We illustrate the results of several machine learning techniques with a system of interdependent water, gas, and power utilities in Shelby County, TN and implement two types of disruption: random and spatial. The different predictive techniques used are a linear model, decision trees, gradient boosting, and random forest, which provided consistent predictions. To portray the consistency of prediction, 30 random samples (a widely accepted sample size) were trained, predicted and the results were compared. Linear model provided the best prediction results for both random and spatial disruptions with a mean RMSE of 3.8, mean correlation of 0.92 and mean bias of 0.012 for the random disruption, and mean RMSE of 1.15, mean correlation of 0.99 and mean bias of − 0.002 for the spatial disruption.