Landslide Mitigation Informatics (LIMIT): Effective decision-making for complex landslide geohazards.

滑坡缓解信息学（LIMIT）：复杂滑坡地质灾害的有效决策。

• 批准号: NE/T00567X/1
• 负责人: Stuart Dunning
• 金额: $ 15.3万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT00567X%2F1
• 关键词: Landslide; Mitigation; Informatics; LIMIT; Effective

Landslides or the threat of landslides can cause significant economic disruption and pose a risk to life. Relatively small events can affect wide areas, particularly where the primary road network is sparse and there is limited scope for rerouting and diversion. Rainfall triggers the majority of landslides in the U.K. and national level 24-hr forecasts exist (for emergency response agencies), but there is uncertainty surrounding what combination(s) of duration and intensity trigger slope failures on a site specific level and why similar events do not always lead to the same event/no-event outcome. These knowledge gaps are critical where decisions must actively be made to warn users of (or close) linear infrastructure such as roads and rail in order to saves lives and costs. This lack of specificity, combined with the high costs of traditionally instrumenting known 'at risk' locations, hinders effective decision-making for key authorities and their partners. As a result many essential components of the environment are not monitored in advance, or on a wide-scale / high-resolution (spatial and temporal) basis. LIMIT will make use of and develop the next generation of low-cost and low-power integrated network (and networks of networks) sensors combined with edge processing and multi-threshold trigger based streaming of key data in near real-time to allow decisions underpinned by advanced theories of failure mechanics. The result is low cost, wide coverage provision of data that analyses the state of the environment and forecasts future behaviour at higher spatial and temporal resolutions than previously possible, integrated into a seamless 'data chain' from site to decision-makers. Data and key derivations based on fundamental process science are automatically ingested/shared into a newly constructed digital environment via an intelligent hierarchical platform. The outputs are fit for national data sets and modelling; policy makers deciding on sensor networks for monitoring evolving risk due to long-term environmental changes; operational decision-makers tasked with real-time management of acute threats to life; right though to data provision and two-way engagement with the individuals at risk. Innovative low-cost, in situ near real-time data streaming/processing sensors resiliently linked to an integrated portal with automated reporting offers a viable and transformative solution to end-user challenges.The LIMIT feasibility study will generate new field validated intelligent monitoring informatics, underpinned by advanced theories of failure mechanics, to provide critical data on the increasing likelihood and then the occurrence of slope failures in real-time.

山体滑坡或山体滑坡的威胁可能会造成严重的经济混乱，并对生命构成威胁。相对较小的事件可能会影响到大范围的地区，特别是在主要公路网稀疏、改道和改道的空间有限的地方。降雨引发了英国的大部分山体滑坡，国家层面存在24小时预报(对于应急机构)，但对于持续时间和强度的组合(S)在特定场地水平上触发斜坡破坏存在不确定性，以及为什么类似事件并不总是导致相同的事件/非事件结果。这些知识差距至关重要，因为必须积极做出决定，警告用户注意(或关闭)公路和铁路等线性基础设施，以挽救生命和节省成本。这种缺乏针对性，再加上传统上利用仪器测量已知的“危险”地点的高昂成本，阻碍了主要当局及其合作伙伴的有效决策。因此，环境的许多基本组成部分没有事先监测，或者没有在大范围/高分辨率(空间和时间)的基础上监测。Limit将利用和开发下一代低成本、低功耗的集成网络(和网络网络)传感器，结合边缘处理和基于多阈值触发的近实时关键数据串流，以实现以先进故障力学理论为基础的决策。其结果是提供低成本、覆盖面广的数据，以比以前更高的空间和时间分辨率分析环境状态和预测未来行为，并集成到从现场到决策者的无缝“数据链”中。基于基本过程科学的数据和关键派生通过智能分层平台自动地摄取/共享到新构建的数字环境中。这些产出适用于国家数据集和建模；政策制定者决定使用传感器网络来监测长期环境变化引起的不断变化的风险；业务决策者负责实时管理生命的严重威胁；数据的提供和与面临风险的个人的双向接触。创新的低成本、就地接近实时的数据流/处理传感器弹性地链接到具有自动报告功能的集成门户，为终端用户的挑战提供了一种可行且具有变革性的解决方案。极限可行性研究将产生新的现场验证的智能监测信息学，以故障力学的先进理论为基础，提供关于不断增加的可能性以及随后发生的斜坡故障的实时关键数据。

项目成果

Detection and forecasting of shallow landslides: lessons from a natural laboratory

浅层滑坡的检测和预报：自然实验室的经验教训

• DOI: 10.1080/19475705.2022.2041108
• 发表时间: 2022
• 期刊: Geomatics, Natural Hazards and Risk
• 作者: Bainbridge R

Low-Cost Automatic Slope Monitoring Using Vector Tracking Analyses on Live-Streamed Time-Lapse Imagery

• DOI: 10.3390/rs13050893
• 发表时间: 2021-02
• 期刊: Remote. Sens.
• 作者: Muhammad Waqas Khan;S. Dunning;Rupert Bainbridge;James Martin;A. Diaz-Moreno;H. Torun;Nanlin Jin;J. Woodward;M. Lim