Participatory Statistical Inference of Interdependent Critical Infrastructure Recovery Times

这项研究将开发新的方法来估计相互依赖的关键基础设施，如电力和供水系统，在破坏灾害事件后的灾难恢复时间。目前的复苏估计通常更多的是即兴的，而不是系统性的和统计上的严谨。该项目将系统和严格地应对这一重要挑战。它将导致开发灾难恢复时间估计和受灾害影响的关键基础设施系统规划的开放源码工具，重点是确保易用性。该框架的项目案例研究将支持工具的开发，扩大我们对灾后基础设施恢复的理解，并帮助建立可供其他社区效仿的最佳做法。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，该项目团队将利用现有的两个项目，目标是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，以促进未来改善美国社区及其关键基础设施系统复原力的规划举措。该项目开发了一个新的方法框架，以及支持这一框架的软件工具，用于估计灾后相互依存的关键基础设施恢复时间。该框架的核心是从专题专家那里获得复苏估计的参与性进程。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。将使用以人为中心的方法来开发软件，以最大限度地提高用户体验和启发性能。该方法框架将使用贝叶斯推理将现有的经验数据与专家估计相结合。专家将估计复苏函数或趋势，而不是点或概率分布。这将允许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西雅图、华盛顿州和俄勒冈州波特兰的案例研究进行评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.