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Flood-PREPARED: Predicting Rainfall Events by Physical Analytics of REaltime Data

Flood-PREPARED: Predicting Rainfall Events by Physical Analytics of REaltime Data
洪水准备:通过实时数据的物理分析预测降雨事件
批准号:
NE/P017134/1
负责人:
Stuart Barr
金额:
$194.13万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2017
资助国家:
英国
项目状态:
已结题
起止时间:
2017 至 --

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中文摘要
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英文摘要
Flood-PREPARED will develop an international leading capability for real-time surface water flood risk and impacts analysis for cities. Our vision is to provide the tools and methods that allow cities to become proactive, rather than be reactive, to managing surface water flooding This will be achieved by developing new physical analytical methods that integrate advanced urban flood hazard models with statistical analytics of big data from multiple real-time data-feeds that describe the current state and condition of the city in terms of surface water flood risk and impacts. By coupling physically-based modelling and statistical analytics, decision makers will be provided with improved real-time predictions of surface water flooding to assist in flood mitigation at a range of governance scales; from the individual site through to national emergency and response. This vision will be delivered via five interrelated work packages:Work package 1 will develop the data management platforms required for capturing, managing and making available the wide variety of real-time data that will be utilised, including real-time weather radar, environmental weather station data feeds, sewer telemetry gauging, CCTV data and traffic congestion data. Data comes from Newcastle's £1.5m Urban Observatory that includes hundreds of pervasive environmental sensors that currently record ~1million observations per day. Work package 2 will employ this data within a new hydrodynamic surface water flood model that employs statistical data assimilation and modelling for improved real-time calibration and parameterisation for surface water flooding. The outputs of the hydrodynamic surface water flood model will be used in work package 3 to parameterise real-time impacts analysis. Using real-time data feeds such as CCTV, social media, traffic monitoring, new predictive models of how impacts evolve and cascade within cities will be developed for improved response and mitigation.Work package 4 will develop the integrated computational workflow and scheduling software required for the tools and methods of work package 2 and 3 to be employed in an operational manner. An operational 'live' demonstrator of the system will be implemented in work package 5. Working with key strategic project partners the demonstrator will be rigorously evaluated through a series of case studies at the individual site, city and national scale to evaluate how improved surface flood risk mitigation in real-time can be undertaken.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
Software Systems Approach to Multi-Scale GIS-BIM Utility Infrastructure Network Integration and Resource Flow Simulation
多尺度 GIS-BIM 公用事业基础设施网络集成和资源流模拟的软件系统方法
DOI: 10.3390/ijgi7080310
发表时间: 2018
期刊: ISPRS International Journal of Geo-Information
影响因子: 3.4
作者: [Gilbert T]
通讯作者: Gilbert T
Topological integration of BIM and geospatial water utility networks across the building envelope
整个建筑围护结构中 BIM 和地理空间供水网络的拓扑集成
DOI: 10.1016/j.compenvurbsys.2020.101570
发表时间: 2021
期刊: Computers, Environment and Urban Systems
影响因子: --
作者: [Gilbert T]
通讯作者: Gilbert T
Effect of vegetation treatment and water stress on evapotranspiration in bioretention systems
植被处理和水分胁迫对生物滞留系统蒸散量的影响
DOI: 10.1016/j.watres.2024.121182
发表时间: 2024
期刊: Water Research
影响因子: 12.8
作者: [De-Ville S]
通讯作者: De-Ville S
A Real-time Traffic Routing Framework for Flood Risk Management Using Live Urban Observation Data
使用实时城市观测数据进行洪水风险管理的实时交通路由框架
DOI: 10.5194/egusphere-egu2020-5194
发表时间: 2020
期刊:
影响因子: --
作者: [Dong N]
通讯作者: Dong N
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