NNA Track 2: Collaborative Research: Planning for Infrastructure Resiliency and Adaptation amid Increasing Mass-Movement Risks across the Cryosphere

导航新北极（NNA）是美国国家科学基金会的十大构想之一。NNA项目解决快速变化的北极地区的融合科学挑战。北极研究需要为国家、更大地区和全球的经济、安全和复原力提供信息。NNA支持从地方到国际规模的新研究伙伴关系，使下一代北极研究人员多样化，加强正式和非正式教育的努力，并在适当情况下整合知识的共同生产。该奖项通过支持具有明确潜力的规划活动，以开发新颖，领先的研究理念和方法来实现NNA目标，从而实现了该目标的一部分。它整合了自然和建筑环境的各个方面，以解决这个十字路口的重要社会挑战，并与国际和当地社区合作。不断变化的气候条件增加了冰冻圈中大规模运动危害的发生，如山体滑坡、泥石流和因富含冰的永久冻土融化而导致的边坡破坏。跨越冰冻圈的大规模移动危害对人和基础设施（如高速公路和管道）构成重大风险。该项目汇集了来自不同背景的专家，包括工程师，地球科学家，计算机科学家，以及来自各种学术机构，公共和政府机构以及行业的官员，讨论关键挑战并制定研究重点：1)表征冰冻圈中的大规模运动危害，2)使用机器学习绘制此类危害，3)使用人工智能预测此类危害。4)通过灵活和适应性的方法建设适应气候变化的基础设施，以降低成本。项目成果使规划者和政策制定者能够在规划城市和农村附近的未来基础设施时，确定北极、亚北极和山区以及高危险区和低危险区受大规模运动危害威胁最大的关键基础设施。该奖项资助了一个融合研究团队的发展和规划活动，以解决基础设施的弹性和适应能力，以应对随着气候变暖，整个冰冻圈日益增加的大规模移动风险。规划活动围绕两个年度研讨会展开，这些研讨会将确定我们目前的理解和现有方法的差距，以开发：1)通过遥感和机器学习，包括卷积神经网络等完善的工具，实现更自动化的灾害绘图工具；2)数据集和数据库的汇编以及与潜在用户的互动；3)更可靠的人工智能模型，包括用于灾害预测的时间序列机器学习；4)更灵活和适应性的方法，以降低基础设施弹性和适应性的成本。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。

Navigating the New Arctic (NNA) is one of NSF's 10 Big Ideas. NNA projects address convergence scientific challenges in the rapidly changing Arctic. The Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, enhances efforts in formal and informal education, and integrates the co-production of knowledge where appropriate. This award fulfills part of that aim by supporting planning activities with clear potential to develop novel, leading edge research ideas and approaches to address NNA goals. It integrates aspects of the natural and built environments to address important societal challenges at this intersection, and engages internationally and with local communities.Changing climate conditions have increased the occurrence of mass-movement hazards in the cryosphere, such as landslides, debris flows, and slope failures resulting from thawing of ice-rich permafrost. Mass-movement hazards across the cryosphere pose a significant risk to people and infrastructure, such as highways and pipelines. This project brings together experts from diverse backgrounds, including engineers, geoscientists, computer scientists, and officials from a variety of academic institutions, public and government agencies, and industry, to discuss key challenges and formulate research priorities in 1) characterizing mass-movement hazards in the cryosphere, 2) mapping such hazards using machine learning, 3) forecasting such hazards using artificial intelligence, and 4) building climate-change-resilient infrastructure through flexible and adaptive approaches to reduce costs. The project outcomes enable planners and policy makers to identify critical infrastructure that is most threatened by mass-movement hazards in the Arctic, sub-Arctic, and mountainous regions, and high- and low-hazard areas when planning future infrastructure near urban and rural sites.This award funds the development and planning activities of a convergent research team to address infrastructure resilience and adaptation to increasing mass-movement risks across the cryosphere as the climate warms. Planning activities center around two annual workshops that will identify gaps in our current understanding and existing methodologies to develop: 1) more automated hazard mapping tools through remote sensing and machine learning, including well-established tools like convolutional neural networks; 2) compilations of datasets and databases and interactions with potential users; 3) more reliable artificial intelligence models including time series machine learning for hazard forecasting; and 4) more flexible and adaptive approaches to reduce costs for infrastructure resiliency and adaptation.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.