Participatory Statistical Inference of Interdependent Critical Infrastructure Recovery Times

这项研究将开发新的方法来估计电力和水系统等相互依赖的关键基础设施在破坏性灾害事件发生后的灾难恢复时间。当前的恢复估计通常比系统的和统计上严格的还要特别。该项目将系统而严谨地应对这一重要挑战。它将导致开源工具，用于开发灾难恢复时间估计和规划受危险影响的关键基础设施系统，重点是确保易用性。该框架的项目案例研究将为工具的开发提供基础，扩大我们对灾后基础设施恢复的理解，并有助于建立可供其他社区效仿的最佳实践。为了培养年轻研究人员和未来实践者的能力，项目团队将在整个项目中整合本科生和研究生。为了让传统上代表性不足的学生参与STEM教育，项目团队将利用现有的两个项目，分别针对高中生和即将入学的大学生。因此，这项科学研究支持了NSF的使命，即促进科学进步，促进我们国家的福利和繁荣，从而促进未来的规划举措，以提高美国社区及其关键基础设施系统的弹性。该项目开发了一个新的方法框架，以及支持该框架的软件工具，用于估计事件后相互依赖的关键基础设施恢复时间。该框架的核心是一个参与性进程，以征求专题专家对恢复情况的估计。该框架将包括基于平板电脑和基于网络的软件工具，以促进启发。以人为中心的方法将用于开发软件，以最大限度地提高用户体验和启发性能。方法框架将使用贝叶斯推理来整合可用的经验数据与专家估计。专家将估计恢复函数或趋势，而不是点或概率分布。这将能够计算常见的弹性指标，例如恢复曲线下的面积。该框架和工具将根据华盛顿州西雅图和俄勒冈州波特兰的案例研究进行评估。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This research will develop new methods to estimate disaster recovery times of interdependent critical infrastructures, such as electricity and water systems, after damaging hazard events. Current recovery estimation is typically more ad hoc than systematic and statistically rigorous. This project will address this important challenge systematically and with rigor. It will lead to open-source tools for developing disaster recovery time estimates and planning for hazard-impacted critical infrastructure systems, with a focus on ensuring ease of use. Project case studies of the framework will underpin development of the tools, expand our understanding of infrastructure recovery after disasters, and help establish best practices that can be emulated by other communities. To build capacity of young researchers and future practitioners, the project team will integrate undergraduate and graduate students throughout the project. To engage traditionally underrepresented students in STEM education, the project team will leverage two existing programs targeting high school and incoming college students. This scientific research thus supports NSF's mission to promote the progress of science and to advance our national welfare and prosperity with benefits that will facilitate future planning initiatives to improve the resilience of United States communities and their critical infrastructure systems.The project develops a new methodological framework, as well as software tools to support this framework, for estimating post-event interdependent critical infrastructure recovery times. The core of the framework is a participatory process for eliciting recovery estimates from topical experts. The framework will include tablet-based and web-based software tools to facilitate the elicitation. A human-centered approach will be used to develop the software to maximize user experience and elicitation performance. The methodological framework will use Bayesian inference to integrate available empirical data with expert estimates. Experts will estimate recovery functions or trends, rather than points or probability distributions. This will enable calculation of common resilience metrics, such as the area under a recovery curve. The framework and tools will be evaluated based on case studies in Seattle, WA and Portland, OR.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.