Global infrastructure flood risk analysis using big data

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.