NSF Convergence Accelerator: Symposium on Big Data and AI-Driven Disaster Management for Planning, Response, Recovery, and Resiliency

该项目支持组织和举办研讨会，以扩大希望更有效地使用大数据和人工智能工具的利益相关者之间的合作，以更有效地准备，应对和恢复自然灾害。仅在2017年，美国就经历了16次以上的自然灾害，每一次造成的影响都超过10亿美元，许多预测都指出，灾害将更加频繁，更加严重。正因为如此，组织和研究工作都是针对改善规划，即时响应和抵御自然灾害的弹性。然而，这些努力往往是孤立的基础上的灾害类型（如野火与山体滑坡）。另一个挑战是理解和使用技术能力的爆炸，这些技术能力提供了如此多的新数据源，包括物理（例如，传感器、物联网工具、无人机），网络（例如，灾害数据库、开放式政府数据）和各种形式（文本/图像/视频）的社交（例如微博流）数据。本次研讨会将确定未来研究和组织间合作的领域，这些领域是将大数据、人工智能和机器学习工具转化为各种灾害管理的可操作知识所必需的。 该研讨会将汇集30-40名利益相关者，包括从业人员（例如，第一反应者、地方/州/联邦政府）、学术界（包括计算机、社会、物理科学领域）。与工程和技术部门）和行业（营利性、非营利性）合作，讨论从规划到应对再到恢复的抗灾能力所有阶段的信息和数据技术需求。讲习班将确定主要的研究挑战/机会以及研究领域在短期内过渡到实际应用的潜力。这些见解将有助于描述该领域作为NSF融合加速器未来主题的潜力。需要利用人工智能（AI）工具来提高生产力，效率和灾难准备和响应的决策，但大数据和人工智能也与我们社会面临的许多其他挑战有关。因此，本次研讨会还应提供一个例子，说明如何整合不同的数据和工具，以支持规划和决策。这项工作建立在NSF其他项目的投资基础上，包括关键弹性相互依赖基础设施系统和过程（CRISP），危险和灾害跨学科研究（Hazard-SEES）和智能&互联社区（S& CC）。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评估被认为值得支持。影响审查标准。

This project supports efforts to organize and host a workshop to expand collaboration between stakeholders wishing to more effectively use big data and artificial intelligence tools to more effectively prepare for, respond to, and recover from natural disasters. The United States experienced more than sixteen natural hazards causing greater than a billion dollars of impact each in 2017 alone, and many predictions point to more frequent and more severe disasters. Because of this organizational and research efforts are directed toward improving planning, immediate response, and resiliency against natural disasters. However, those efforts are often siloed based on the type of disaster (e.g. wildfires vs. landslides). An additional challenge is understanding and using the explosion of technological capabilities that provide so many new data sources, including physical (e.g., sensors, internet-of-things tools, drones), cyber (e.g., hazard databases, open government data), and social (e.g. microblog streams) data of various modalities (text/images/videos). This workshop will identify areas of future research and inter-organizational collaboration that are needed to transform big data, artificial intelligence, and machine learning tools into actionable knowledge for all types of disaster management. This workshop will bring together 30-40 stakeholders including practitioners (e.g. first responders, local/state/federal government), academia (including fields of computer, social, physical sciences. and engineering), and industry (for-profit, non-profit) to discuss the information and data technology needs for all phases of disaster resilience, from planning to response to recovery. The workshop will identify major research challenges / opportunities and the potential for the research areas to transition to practical use in the short term. These insights will help describe the potential for this area to serve as a future topic for the NSF Convergence Accelerator. Big data harnessed with artificial intelligence (AI) tools are needed to improve productivity, efficiency, and decision making in disaster preparedness and response, but big data and AI are also relevant to many other challenges our society is facing. Therefore, this workshop should also provide an example of how disparate data and tools may be integrated for planning and decision support. This effort builds upon investments made by other programs at NSF, including Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP), Interdisciplinary Research in Hazards and Disasters (Hazard-SEES), and Smart & Connected Communities (S&CC), as well as investments by many other U.S. federal agencies.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.