Predicting Resilience with Neural Networks

TL;DR

This paper demonstrates that neural network models, especially LSTMs, significantly outperform traditional statistical methods in predicting system resilience and recovery times after disruptive events.

Contribution

It introduces three neural network approaches for resilience prediction, showing their superior performance over classical models in real-world data.

Findings

LSTMs achieved over 60% higher adjusted R squared.

Neural networks reduced predictive error by 34 times.

Models effectively incorporate positive and negative resilience factors.

Abstract

Resilience engineering studies the ability of a system to survive and recover from disruptive events, which finds applications in several domains. Most studies emphasize resilience metrics to quantify system performance, whereas recent studies propose statistical modeling approaches to project system recovery time after degradation. Moreover, past studies are either performed on data after recovering or limited to idealized trends. Therefore, this paper proposes three alternative neural network (NN) approaches including (i) Artificial Neural Networks, (ii) Recurrent Neural Networks, and (iii) Long-Short Term Memory (LSTM) to model and predict system performance, including negative and positive factors driving resilience to quantify the impact of disruptive events and restorative activities. Goodness-of-fit measures are computed to evaluate the models and compared with a classical statistical model, including mean squared error and adjusted R squared. Our results indicate that NN models outperformed the traditional model on all goodness-of-fit measures. More specifically, LSTMs achieved an over 60\% higher adjusted R squared, and decreased predictive error by 34-fold compared to the traditional method. These results suggest that NN models to predict resilience are both feasible and accurate and may find practical use in many important domains.