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/T005653/1
• 负责人: Michael Lim
• 金额: $ 15.53万
• 依托单位: Northumbria University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT005653%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造成严重的经济破坏，并对生命构成威胁。相对较小的事件可影响广大地区，特别是在主要公路网稀疏、改道和改道的余地有限的地方。降雨引发了英国大部分的山体滑坡。和国家一级的24小时预报存在（应急响应机构），但存在不确定性的持续时间和强度的组合触发斜坡故障的网站特定的水平，为什么类似的事件并不总是导致相同的事件/无事件的结果。这些知识差距至关重要，必须积极做出决定，警告用户（或关闭）线性基础设施，如公路和铁路，以挽救生命和成本。这种缺乏具体性的情况，再加上传统上对已知的“危险”地点进行监测的高昂成本，阻碍了关键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，也没有在大规模/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（和网络的网络）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流，以允许基于故障力学先进理论的决策。其结果是提供低成本、覆盖面广的数据，分析环境状况，并以比以往更高的空间和时间分辨率预测未来行为，并将其纳入从现场到决策者的无缝“数据链”。基于基础过程科学的数据和关键推导通过智能分层平台自动摄取/共享到新构建的数字环境中。这些产出适合国家数据集和建模;决策者决定传感器网络以监测由于长期环境变化而产生的不断变化的风险;运营决策者负责实时管理对生命的严重威胁;正确提供数据并与处于风险中的个人进行双向接触。创新的低成本、现场近实时数据流/处理传感器弹性地连接到集成门户，并提供自动报告，为最终用户的挑战提供了一个可行的变革性解决方案。LIMIT可行性研究将产生新的现场验证的智能监测信息，以先进的失效力学理论为基础，以提供有关斜坡崩塌的可能性增加及即时发生的关键数据。

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

Laplacian Scores-Based Feature Reduction in IoT Systems for Agricultural Monitoring and Decision-Making Support.

• DOI: 10.3390/s20185107
• 发表时间: 2020-09-08
• 期刊: Sensors (Basel, Switzerland)
• 作者: Tsapparellas G;Jin N;Dai X;Fehringer G
• 通讯作者: Fehringer G

The predictability of shallow landslides: lessons from a natural laboratory

浅层滑坡的可预测性：来自自然实验室的教训

• 发表时间: 2020
• 期刊: Earth ArXiv
• 作者: Bainbridge R