Delivering resilient power, road and rail networks by translating a tree failure risk model for multi-sector applications.

通过将树木倒塌风险模型转化为多部门应用，提供有弹性的电力、道路和铁路网络。

• 批准号: NE/N012984/1
• 负责人: George Blackburn
• 金额: $ 8.4万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FN012984%2F1
• 关键词: Delivering; resilient; power; road; rail

When storms cause trees to fall onto power lines, roads and railways this can pose serious threats to human life and disruption to electricity supplies and transport leading to large financial costs to both operators and users of these networks. There is growing evidence that climate change will lead to an increase in storminess in the UK so the problems associated with tree failure are likely to grow. This project aims to use a computerised system for predicting which trees are likely to fall onto powerlines, roads and railways during the types of storms that typically occur in the UK. This will allow the operators of these infrastructure networks to fell those trees that are most likely to fail and cause disruption. The project will make use of newly-developed techniques which employ airborne laser scanners to map trees and measure their key properties, which will improve our ability to estimate the susceptibility of trees to failure. So the outputs of the system will enable the operators of infrastructure networks to pro-actively manage trees in order to improve the resilience of their infrastructure to future storms. The intensity and impacts of storms vary considerably over space and time so it is not possible to manage trees for all possible conditions. Therefore we will develop the tree failure prediction system so that it is able to use short-range weather forecasts (up to 5 days) which are the most reliable predictions of impending storm events. This will enable the system to predict which trees are likely to fail and cause disruptions to infrastructure networks during the forthcoming storm conditions. This information will help the network operators to draw up effective plans for responding to and recovering from storms, e.g. by organising field teams to be in the locations where greatest tree damage is likely to occur so they can remove fallen debris and repair the infrastructure. To be effective our tree failure prediction system will need to operate quickly and repeatedly so it can respond to regular updates in weather forecasts as storms develop. Also it needs to incorporate assessments of the large number of trees which surround the power, road and rail networks in the UK. Therefore, to achieve this, we will make use of the very powerful cloud-based computing technology that is now rapidly developing. The outputs of our system will be conveyed to users via an interactive web page which will support strategic decision-making and a mobile app that will support field teams.Keywords: tree failure, storm, prediction, power supply, road, rail, decision-making, resilience.The following organisations are stakeholders in the project and will form an advisory board to oversee our work: UK Power Networks, Scottish Power, Transport Scotland, Scottish Water, Bluesky International, Atkins Global.

当风暴导致树木倒在电线、公路和铁路上时，这可能对人类生命构成严重威胁，并中断电力供应和运输，给这些网络的运营商和用户带来巨大的财务成本。越来越多的证据表明，气候变化将导致英国暴风雨的增加，因此与树木失败相关的问题可能会增加。该项目旨在使用一个计算机化系统来预测在英国通常发生的风暴类型期间哪些树木可能会倒在电力线，公路和铁路上。这将使这些基础设施网络的运营商能够砍伐那些最有可能失败并导致中断的树木。该项目将利用新开发的技术，利用机载激光扫描仪绘制树木地图并测量其主要属性，这将提高我们估计树木易受破坏的能力。因此，该系统的输出将使基础设施网络的运营商能够主动管理树木，以提高其基础设施对未来风暴的抵御能力。风暴的强度和影响在空间和时间上变化很大，因此不可能在所有可能的条件下管理树木。因此，我们将开发树木故障预测系统，以便能够使用短期天气预报（最多5天），这是即将发生的风暴事件的最可靠的预测。这将使系统能够预测哪些树木可能会在即将到来的风暴条件下失败并导致基础设施网络中断。这些信息将有助于网络运营商制定有效的计划，以应对风暴并从风暴中恢复，例如组织现场团队前往可能发生最严重树木破坏的地点，以便他们可以清除倒下的碎片并修复基础设施。为了有效，我们的树木故障预测系统将需要快速和重复运行，以便它可以响应风暴发展时天气预报的定期更新。它还需要纳入对英国电力、公路和铁路网络周围大量树木的评估。因此，为了实现这一目标，我们将利用目前正在快速发展的非常强大的基于云的计算技术。我们的系统将通过一个互动网页和一个移动的应用程序向用户传达结果，该网页将支持战略决策，该应用程序将支持现场团队。关键词：树木故障，风暴，预测，电力供应，道路，铁路，决策，复原力。以下组织是项目的利益相关者，并将成立一个咨询委员会来监督我们的工作：英国电力网络公司、苏格兰电力公司、苏格兰交通公司、苏格兰水务公司、蓝天国际公司、阿特金斯全球公司。

项目成果

ARBOR: A new framework for assessing the accuracy of individual tree crown delineation from remotely-sensed data

• DOI: 10.1016/j.rse.2019.111256
• 发表时间: 2019-09
• 期刊: Remote Sensing of Environment
• 影响因子: 13.5
• 作者: Jon Murray;D. Gullick;G. A. Blackburn;J. D. Whyatt;Christopher Edwards

Tree risk evaluation environment for failure and limb loss (TREEFALL): An integrated model for quantifying the risk of tree failure from local to regional scales

• DOI: 10.1016/j.compenvurbsys.2019.02.001
• 发表时间: 2019-05-01
• 期刊: COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
• 影响因子: 6.8
• 作者: Gullick, D.;Blackburn, G. A.;Abbatt, J.
• 通讯作者: Abbatt, J.