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

新北极航行（NNA）是NSF的十大创意之一。NNA项目解决快速变化的北极地区的趋同科学挑战。北极研究需要为国家、更大地区和地球仪的经济、安全和复原力提供信息。NNA授权从地方到国际规模的新的研究伙伴关系，使下一代北极研究人员多样化，加强正规和非正规教育的努力，并在适当的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来实现这一目标的一部分，以开发新颖，前沿的研究思路和方法来实现NNA目标。它整合了自然和人造环境的各个方面，以应对这一交叉点上的重要社会挑战，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质量运动危害的发生，如山体滑坡，泥石流和富含冰的永久冻土融化造成的边坡破坏。穿越冰冻圈的大规模移动危害对人员和基础设施（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优先事项：1）描述冰冻圈中的质量运动危害，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）通过灵活和适应性强的方法建设抵御气候变化的基础设施，以降低成本。该项目的成果使规划者和决策者能够确定在北极、亚北极和山区受到大规模运动危害威胁最大的关键基础设施，在规划城市和农村地区附近的未来基础设施时，考虑到高风险和低风险地区。该奖项资助一个聚合研究团队的开发和规划活动，以解决基础设施的弹性和适应日益增长的质量，随着气候变暖，穿越冰冻圈的运动风险。规划活动围绕两个年度研讨会进行，这些研讨会将确定我们目前的理解和现有方法中的差距，以开发：1）通过遥感和机器学习，包括卷积神经网络等成熟工具，实现更自动化的灾害测绘工具; 2）汇编数据集和数据库，并与潜在用户进行互动; 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;和4）的方法该奖项反映了NSF的法定使命，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，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.