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.

本研究提出三种替代神经网络方法来模拟和预测系统性能，包括负面和正面因素对于抵御破坏性事件和恢复活动的影响，结果表明神经网络模型在所有适配性评估指标上优于传统模型，特别是长短期记忆模型在可调整 R 方面表现出超过 60% 的提高并将预测误差降低了 34 倍。这些结果表明神经网络模型预测弹性具有可行性和准确性，并可能在许多重要领域中找到实际用途。