论文标题
基础架构恢复曲线使用高斯流程回归对专家的数据进行回归估算
Infrastructure Recovery Curve Estimation Using Gaussian Process Regression on Expert Elicited Data
论文作者
论文摘要
基础架构恢复时间估计对于灾难管理和计划至关重要。受到最近的弹性计划计划的启发，我们考虑了一种情况，专家被要求估算不同基础架构系统在情况危害事件后恢复到某些功能水平的时间。我们提出了一个方法学框架，以使用专家吸引的数据来估计特定基础架构系统的预期恢复时间曲线。该框架使用高斯过程回归（GPR）来捕获专家的估计 - 确定性，并满足恢复过程的已知物理约束。该框架旨在在专家启发的数据收集成本与GPR的预测准确性之间找到平衡。我们评估了有关两次案例研究事件的现实模拟专家吸引数据的框架，即1995年的大汉辛 - 阿瓦吉地震和2011年的大东日本地震。
Infrastructure recovery time estimation is critical to disaster management and planning. Inspired by recent resilience planning initiatives, we consider a situation where experts are asked to estimate the time for different infrastructure systems to recover to certain functionality levels after a scenario hazard event. We propose a methodological framework to use expert-elicited data to estimate the expected recovery time curve of a particular infrastructure system. This framework uses the Gaussian process regression (GPR) to capture the experts' estimation-uncertainty and satisfy known physical constraints of recovery processes. The framework is designed to find a balance between the data collection cost of expert elicitation and the prediction accuracy of GPR. We evaluate the framework on realistically simulated expert-elicited data concerning the two case study events, the 1995 Great Hanshin-Awaji Earthquake and the 2011 Great East Japan Earthquake.
