SCC-PG : Human-AI Teaming for Flood Evacuation Decision Making

SCC-PG：人机协作进行洪水疏散决策

• 批准号: 2125283
• 负责人: Vidya Samadi
• 金额: $ 15万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125283&HistoricalAwards=false
• 关键词: SCC; PG; Human; AI; Teaming

Evacuation of coastal communities, particularly in rural areas, can be a challenge due to the topography, dispersed residential and population patterns, and limited number of roadways that lead further inland. This Smart and Connected Community Planning project will assess the viability of leveraging human knowledge and social media data with an artificial intelligence (AI) system to create a human-AI teaming (HAT) paradigm addressing flood evacuation decision making in isolated coastal rural communities. In the process of creating a HAT paradigm, we will integrate transportation network data with river information as well as volunteers’ observations and social media data to leverage the strengths of local members of a Community Emergency Response Team (CERT) with AI. This Planning project advances the field by conducting a feasibility study of this HAT decision-making tool, which will be tested through real-world flood evacuation examples in Charleston, Berkeley, and Dorchester Counties located in the Lowcountry region of South Carolina (SC). Additionally, we will determine the barriers and motivations for understanding the usability of the researched HAT decision-making tool using qualitative (interview) protocols. This project supports education and diversity by providing research experiences to diverse students, as well as focusing on vulnerable, rural communities. Additionally, this planning project supports NSF's mission to promote the progress of science and to advance the nation's health, prosperity, and welfare by seeking to (i) enhance flood evacuation by automatic data-driven decision making and (ii) identify potential barriers to the adoption of technology in rural volunteer communities. This research will co-create and implement a pilot solution for flood evacuation decision making by including systems thinking, human–machine engagement, human development training, and AI-driven decision making. The focus is on the advancement of flood evacuation techniques by transferring the traditional determination techniques (the expert evaluation approach) toward new and coherent HAT computing. By leveraging HAT protocols and applying them to a flood evacuation decision-making tool, our project has the potential to be transformative. Tackling these issues will enable us to harness the full potential of AI as a partner in emergency management and response. This planning project brings together researchers from water resources engineering, social sciences and communication, transportation engineering, disaster science, computer science, and numerous volunteers and stakeholders to co-create solutions, build/strengthen collaboration with key stakeholders and CERT organizations and identify potential barriers to technology use in rural communities challenged by a higher incidence of flood hazards and substandard infrastructure.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.

由于地形、分散的居住和人口模式以及通往内陆的道路数量有限，沿海社区，特别是农村地区的疏散可能是一项挑战。这个智能互联社区规划项目将评估利用人类知识和社交媒体数据与人工智能（AI）系统的可行性，以创建一个人类-人工智能团队（HAT）范例，解决沿海偏远农村社区的洪水疏散决策问题。在创建HAT范例的过程中，我们将整合交通网络数据与河流信息，以及志愿者的观察和社交媒体数据，以利用社区应急响应小组（CERT）当地成员与人工智能的优势。该规划项目通过对HAT决策工具进行可行性研究来推进该领域的发展，该工具将通过位于南卡罗来纳州低乡村地区的查尔斯顿、伯克利和多切斯特县的实际洪水疏散实例进行测试。此外，我们将使用定性（访谈）协议确定理解所研究HAT决策工具的可用性的障碍和动机。该项目通过为不同的学生提供研究经验，以及关注脆弱的农村社区，支持教育和多样性。此外，该规划项目支持NSF的使命，即通过寻求(i)通过自动数据驱动的决策制定加强洪水疏散和（ii）确定在农村志愿者社区采用技术的潜在障碍，促进科学进步和促进国家的健康、繁荣和福利。本研究将通过系统思考、人机互动、人类发展培训和人工智能驱动的决策，共同创建并实施洪水疏散决策的试点解决方案。重点是通过将传统的确定技术（专家评估方法）转移到新的连贯的HAT计算中来推进洪水疏散技术。通过利用HAT协议并将其应用于洪水疏散决策工具，我们的项目具有变革性的潜力。解决这些问题将使我们能够充分利用人工智能作为应急管理和反应伙伴的潜力。该规划项目汇集了来自水资源工程、社会科学和通信、交通工程、灾害科学、计算机科学的研究人员，以及众多志愿者和利益相关者，共同制定解决方案，建立/加强与关键利益相关者和CERT组织的合作，并确定在农村社区使用技术的潜在障碍，这些社区受到洪水灾害发生率较高和基础设施不合格的挑战。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。

项目成果

An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks

• DOI: 10.1029/2022ea002385
• 发表时间: 2023-01
• 期刊: Earth and Space Science
• 影响因子: 3.1
• 作者: L. Windheuser;R. Karanjit;R. Pally;S. Samadi;N. Hubig