CoPe EAGER: Addressing Human-Centric Decision-Making Challenges from Coastal Hazards via Integrated Geosciences Modeling and Stochastic Optimization

CoPe EAGER：通过综合地球科学建模和随机优化解决沿海灾害带来的以人为本的决策挑战

• 批准号: 1940308
• 负责人: Erhan Kutanoglu
• 金额: $ 29.99万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940308&HistoricalAwards=false
• 关键词: CoPe; EAGER; Addressing; Human; Centric

Coastal communities are susceptible to flooding due to tropical storms, hurricanes, and heavy rainfall events. These events have increased recently in frequency and intensity. Therefore, it is critical to develop smart, science-based systems, tools and models that capture the underlying behavior of coastal hazards, and to coordinate and optimize decisions before, during and after natural hazards, to enhance the resilience and response of coastal communities. This research undertakes an exploratory and unifying research agenda focused on integrating geosciences-based modeling of coastal floods with scenario-based stochastic optimization for human-centric decision-making problems that coastal communities face in the wake of hurricanes and other flood-inducing events. Using such events as archetypal coastal hazards, the project addresses a specific human-centric problem: evacuating patients from hospitals and nursing homes just before such hazards. Patient evacuation planning is especially important as mismanagement has several times led to unnecessary deaths in hospitals, in nursing homes, or during evacuation. Development of an effective decision support tool, to be used by regional evacuation coordination agencies, could have wide-ranging impact across the United States in future disasters. Indeed, a primary goal of this research is to create a tool that can be disseminated for national use. The knowledge and tools developed on large-scale multi-hospital patient evacuation will lead to new ways to optimally coordinate limited resources when faced with uncertain but predictable events such as hurricanes. Moreover, this integrated approach is extendable to other coastal logistical problems (e.g., prepositioning emergency supplies, siting shelters, prepositioning repair resources and spares for critical infrastructure recovery) thus initiating new research agendas. This research also features robust collaboration with various organizations, including those involved in weather, hurricane, and flood prediction, and emergency management and evacuation, in order to ensure feasibility and usability of the tools produced. On the educational front, the PIs will create teaching modules on evacuation modeling and develop a new course on humanitarian operations research. This project focuses on a specific problem that significantly affects coastal communities in order to highlight the value of integrating geosciences-based modeling of coastal floods with scenario-based stochastic optimization: optimizing large-scale multi-hospital and nursing home evacuation in response to flood-inducing events. This high-stakes problem needs accurate flood predictions. In particular, this research integrates coupled weather forecast, runoff production, river routing, inundation mapping models (in general, geoscience models) with an underlying stochastic optimization model of the decision-making problem. The main use of the geoscience models will be the rigorous generation of flooding scenarios that will serve as input to the stochastic optimization models. The modular architecture of the Weather Research and Forecasting Model, hydrological modeling system (WRF-Hydro), with the Noah Land Surface model (LSM), will be coupled to a vector-based river routing model (RAPID). The integrated geoscience model will generate statistically-grounded flooding scenarios before a hurricane or heavy rainfall event in order to improve recommendations for resource allocation and logistics decisions (e.g., staging area locations, allocation of medical personnel, allocation/routing of ambulances between sending and receiving facilities, etc.). Finally, recognizing the uncertainty in the hurricane forecasts, this effort generates a series of flood scenarios (instead of a single realization) to be used in the patient evacuation problem, which was not done before. A significant merit of the proposed work is to bring together two research communities that do not usually work closely together: operations research and geosciences modeling. In creating this bridge, the research links the predictive power of geosciences modeling with the prescriptive power of stochastic optimization.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.

沿海社区容易受到热带风暴、飓风和暴雨造成的洪水的影响。这些事件最近在频率和强度上有所增加。因此，至关重要的是开发智能的、以科学为基础的系统、工具和模型，以捕捉沿海灾害的潜在行为，并在自然灾害发生之前、期间和之后协调和优化决策，以提高沿海社区的复原力和应对能力。这项研究进行了一个探索性和统一的研究议程，重点是将基于地球科学的沿海洪水建模与以人为本的决策问题，沿海社区在飓风和其他洪水诱发事件后面临的基于雷诺的随机优化相结合。利用这些事件作为典型的沿海灾害，该项目解决了一个具体的以人为本的问题：在这些灾害发生之前从医院和疗养院疏散病人。病人后送计划尤其重要，因为管理不善多次导致医院、疗养院或后送过程中不必要的死亡。开发一种有效的决策支持工具，供区域疏散协调机构使用，可能会在未来的灾难中对美国产生广泛的影响。事实上，这项研究的一个主要目标是创造一个可以传播供全国使用的工具。在大规模多医院患者后送方面开发的知识和工具将为在面临飓风等不确定但可预测的事件时优化协调有限资源提供新的方法。此外，这种综合方法可扩展到其他沿海物流问题（例如，为重要基础设施的恢复预先部署应急物资、安置避难所、预先部署维修资源和备件），从而启动新的研究议程。该研究还与各种组织进行了强有力的合作，包括参与天气，飓风和洪水预测以及应急管理和疏散的组织，以确保所产生工具的可行性和可用性。在教育方面，PI将创建关于疏散建模的教学模块，并开发关于人道主义行动研究的新课程。该项目的重点是一个具体的问题，显着影响沿海社区，以突出整合基于地球科学的沿海洪水建模与基于随机优化的价值：优化大规模的多医院和疗养院疏散，以应对洪水诱发事件。这个高风险的问题需要准确的洪水预测。特别是，这项研究集成了耦合天气预报，径流生产，河流路由，洪水映射模型（一般来说，地球科学模型）与决策问题的随机优化模型。地球科学模型的主要用途将是严格生成洪水情景，作为随机优化模型的输入。天气研究和预报模型、水文模拟系统（WRF-Hydro）的模块结构，以及诺亚陆地表面模型（LSM），将与基于矢量的河流定线模型（RAPID）相结合。综合地球科学模型将在飓风或强降雨事件发生前生成基于地理的洪水情景，以改进资源分配和物流决策的建议（例如，集结区位置、医务人员的分配、救护车在发送和接收设施之间的分配/路线等）。最后，认识到飓风预报的不确定性，这项工作产生了一系列的洪水场景（而不是一个单一的实现），用于病人疏散问题，这是以前没有做过的。拟议工作的一个重要优点是将两个通常不密切合作的研究团体聚集在一起：运筹学和地球科学建模。在建立这座桥梁的过程中，该研究将地球科学建模的预测能力与随机优化的规定能力联系起来。该奖项反映了NSF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。

项目成果

ADCIRC Simulation of Synthetic Storms in the Gulf of Mexico

ADCIRC 对墨西哥湾合成风暴的模拟

• DOI: 10.17603/ds2-68a9-0s64
• 发表时间: 2021
• 期刊: Designsafe-CI
• 作者: Dawson, Clinton N.;Del-Castillo-Negrete, Carlos;Shukla, Ashutosh;Pachev, Benjamin;Kaiser, Carola;Kutanoglu, Erhan
• 通讯作者: Kutanoglu, Erhan

A Scenario-based Optimization Model for Long-term Healthcare Infrastructure Resilience against Flooding

基于场景的长期医疗基础设施抗洪能力优化模型

• 发表时间: 2022
• 期刊: Proceedings of the IISE Annual Conference and Expo
• 作者: Toplu-Tutay, Gizem;Hasenbein, John J.;Kutanoglu, Erhan
• 通讯作者: Kutanoglu, Erhan

A Large-Scale Patient Evacuation Modeling Framework using Scenario Generation and Stochastic Optimization

使用场景生成和随机优化的大规模患者疏散建模框架

• 发表时间: 2020
• 期刊: IISE Annual Conference
• 作者: Kim, Kyoung Yoon;Kutanoglu, Erhan;Hasenbein, John J;Wu, Wen-Ying;Yang, Zong-Liang
• 通讯作者: Yang, Zong-Liang

Hurricane Scenario Generation for Uncertainty Modeling of Coastal and Inland Flooding

用于沿海和内陆洪水不确定性建模的飓风情景生成

• DOI: 10.3389/fclim.2021.610680
• 发表时间: 2021
• 期刊: Frontiers in Climate
• 作者: Kim, Kyoung Yoon;Wu, Wen-Ying;Kutanoglu, Erhan;Hasenbein, John J.;Yang, Zong-Liang
• 通讯作者: Yang, Zong-Liang

Stochastic Optimization of Large-Scale Patient Evacuation Before Hurricanes

飓风前大规模患者疏散的随机优化

• 发表时间: 2019
• 期刊: IISE Annual Conference
• 作者: Kim, Kyoung Yoon;Kutanoglu, Erhan;Hasenbein, John J.
• 通讯作者: Hasenbein, John J.