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

基本信息

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

项目摘要

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.

由于热带风暴，飓风和大雨事件，沿海社区容易受到洪水的影响。这些事件最近在频率和强度上增加了。因此，开发稳定的基于科学的系统，工具和模型以捕捉沿海危害的潜在行为，并在自然危害期间和之后协调和优化决策以增强沿海社区的韧性和反应，这一点至关重要。这项研究进行了一项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基于场景的基于场景的随机优化，以对以人为中心的决策问题，在飓风和其他造成洪水的事件之后，沿海社区面临这些问题。该项目使用原型沿海危害等事件解决了一个以人为中心的问题：在此类危害之前撤离医院和疗养院的患者。患者疏散计划尤为重要，因为管理不善多次导致医院，疗养院或疏散期间的不必要的死亡。开发有效的决策支持工具，将由区域撤离协调机构使用，在未来的灾难中可能会在美国各地产生广泛的影响。确实，这项研究的主要目标是创建一个可以传播的工具，以供民族使用。在面对不确定但可预测的事件（例如飓风）时，大规模多医院患者疏散的知识和工具将导致最佳协调有限资源的新方法。此外，这种综合方法可扩展到其他沿海后勤问题（例如，介词紧急供应，选址避难所，介词维修资源和备件以进行关键基础设施恢复），从而启动了新的研究议程。这项研究还具有与各种组织的强大合作，包括涉及天气，飓风和洪水预测以及紧急管理和疏散的组织，以确保生产工具的可行性和可用性。在教育方面，PI将创建有关疏散建模的教学模块，并开发有关人道主义运营研究的新课程。该项目着重于一个特定的问题，该问题严重影响沿海社区，以强调将基于地球科学的沿海洪水建模与基于方案的随机优化的价值：优化大规模的多医院和疗养院撤离以响应洪水引起的事件。这个高风险问题需要准确的洪水预测。特别是，这项研究将耦合的天气预报，径流生产，河流路线，淹没映射模型（通常是地球科学模型）与决策问题的潜在随机优化模型相结合。地球科学模型的主要用途将是严格的洪水场景，将作为随机优化模型的输入。天气研究和预测模型的模块化体系结构，水文建模系统（WRF-HYDRO）与Noah Land Surface模型（LSM）将与基于矢量的河流路由模型（快速）耦合。综合地球科学模型将在飓风或大雨事件发生之前生成统计上的洪水场景，以改善有关资源分配和物流决策的建议（例如，分期区域位置，医疗人员的分配，分配/分配/在发送和接收设施之间的救护车分配/路由等）等）。最后，认识到飓风预测的不确定性，这项工作会产生一系列洪水场景（而不是单个实现），该场景将用于患者疏散问题中，这是以前没有进行的。拟议工作的重要优点是将两个通常不紧密合作的研究社区汇集在一起：操作研究和地球科学建模。在建立这座桥时，该研究将地球科学建模的预测能力与随机优化的规范力量联系起来。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来支持的。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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

DOI：
发表时间：
2022
期刊：
Proceedings of the IISE Annual Conference and Expo
影响因子：
0
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

DOI：
发表时间：
2020
期刊：
IISE Annual Conference
影响因子：
0
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
影响因子：
0
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