Global infrastructure flood risk analysis using big data

全球基础设施系统(能源、交通、水、废物和电信)面临严峻挑战。英国和其他地方的分析发现，重大脆弱性、容量限制和资产的使用寿命即将结束。在这种背景下，国际上的政策制定者已经认识到，迫切需要对基础设施进行去碳化，以应对人口、社会和生活方式偏好的变化，并建立应对气候变化日益加剧的影响的韧性。本博士学位将利用以下方面的进展：(I)大规模基础设施风险分析方法；(Ii)描述全球气候及其相关危害、全球风险暴露的现成数据集；以及(Iii)“大数据”处理和云计算技术，以实现第一次全球基础设施风险分析。这项研究将开发一个综合模型，该模型使用来自全球地图来源的数据，如Google，Open Streetmap；i-Cool全球海洋网络(港口洪水)；CAA(机场洪水)；全球洪水风险地图(WRI：洪水.wri.org)和气候模型输出(气候学预测.net)；人口位置(项目的全球农村和城市地图)，以查看未来的风险。该项目将开发一个全球运输网络的综合评估模型，根据所服务的人口、路线类型的信息(如主干道、次要道路等)、其他已公布的信息(如航空公司的航线班次)等来评估主要基础设施网络组成部分的重要性。这些信息与危险程度相结合，将提供独特的全球风险评估。空间数据集的大小需要使用云或分布式计算方法来处理和处理数据。将开发具有网络功能的工具，并在设计用于管理这些网络工具的工作流程的综合框架时考虑到可扩展性，以使其他研究人员能够在获得新的数据和能力时扩充该模型。

Infrastructure systems (energy, transport, water, waste and telecoms) globally face serious challenges. Analysis in the UK and elsewhere identifies significant vulnerabilities, capacity limitations and assets nearing the end of their useful life. Against this backdrop, policy makers internationally have recognised the urgent need to decarbonise infrastructure, to respond to changes in demographic, social and life style preferences, and to build resilience to intensifying impacts of climate change. This PhD will draw on advances in (i) methods for broad scale infrastructure risk analysis, (ii) readily available datasets describing global climate and associated hazards, global exposure, and increasingly information on the location of key infrastructure networks, and, (iii) 'big data' processing and cloud computing techniques, to enable the first global infrastructure risk analysis. The research will develop an integrated model that uses data from global mapping sources such as Google, Open Streetmap; i-COOL global marine networks (port flooding); CAA (airport flooding); global flood hazard maps (WRI: floods.wri.org) and climate model outputs (climateprediction.net); population location (Global Rural Urban Mapping of Project) to look at future risks. The project will develop an integrated assessment model of global transport networks, where the importance of major infrastructure network components are assessed based upon population served, information on route type (e.g. main, secondary road etc.), other published information (e.g. route frequency for airlines) and so on. This information, integrated with hazard extents, will provide a unique global risk assessment. The size of the spatial datasets necessitates a cloud or distributed computing approach to handle and process the data. Web-enabled tools will be developed and the integrated framework for managing the workflow of these web-based tools will be designed with extensibility in mind to enable other researchers to augment the model as new data and capabilities become available.