Ordnance Survey (OS) has worked with Snowflake to develop Intelligent Flood Readiness, an artificial intelligence (AI)-powered data model for assessing the impact of flooding. The model has identified that 1.2 million in England are at risk of falling outside flood protection measures, many in some of the most deprived parts of the country.
The model’s main use is in policymaking, where it is able to support analysis of more granular data instead of treating wide, geographic areas as homogenous when assessing flood risk and remediation. According to Snowflake and OS, policymakers could use the model to identify plans for period properties with basements, for example, and apply that protocol to areas where these properties are shown to exist. It also offers policymakers the ability to assess clusters of vulnerability, especially when these areas straddle arbitrary boundaries such as local authorities or flood risk management plan (FRMP) zones.
The model combines OS’s buildings data with a range of government data and current FRMPs. It combines six entirely separate, critical data streams into a single, shared “structural intelligence” layer.
The initial analysis involved bringing together OS’s building datasets with the Indices of Deprivation in England. Snowflake and OS said the model was able to identify where physical vulnerability (building height and type) intersects with social risk. This was then layered against Environment Agency (EA) flood data, the EA’s Rivers and Sea defended and undefended flood risk extents, and an AI-driven text analysis of over 3,000 pages of statutory FRMP documents.
Snowflake and OS said the model estimated that up to 68% of the buildings identified could be highly vulnerable to the after-effects of flooding – at elevated flood risk, but also located in deprived areas and potentially lacking the resources and social infrastructure to help recover quickly. A contributory factor is likely to be that as much as 84% of these undefended buildings pre-date 2001 – before legislation ensured flood risk was factored into planning permissions.
Yorkshire and the Humber region were idenified by the model as having some of the highest concentrations of vulnerable, undefended properties.
Additionally, the analysis based on the model suggests that 15% of the at-risk premises date from before 1919, and 23% from 1919 to 1959, so were potentially built before their location became a flood risk – underlining just how dynamic England’s natural and built environment is, as captured by OS’s geospatial data.
Tim Chilton, managing geospatial consultant at OS, said: “Ordnance Survey is excited to collaborate with Snowflake to develop an innovative AI model that could help Local Authorities better understand, plan for and manage floods.
“Built on OS’s authoritative and trusted geospatial data and developed using Snowflake’s technology, the model provides insights into how well areas and properties are protected and where to prioritise investment in critical flood defences,” he added.
“By delivering geospatial intelligence difficult to derive manually, decision-makers can access data-driven, actionable insights – without the burden of analysing endless spreadsheets. The model maps vulnerable zones and identifies areas at greatest risk, helping local government shape policy, direct resources and safeguard communities.”
The data also suggests that 85% of at-risk, undefended buildings are vulnerable to surface water flooding, rather than river or coastal flooding. Snowflake and OS said this implies that high-density, multi-unit residential buildings may account for more at-risk households than those in more obviously threatened seafront or riverside locations.
Fawad Qureshi, global field chief technology officer at Snowflake, said: “Data is at the heart of making informed decisions. As this project shows, it’s rare that one body holds all the relevant data or that this data is in the same format. But we’re now in an era where technology can bring together the right people and the right data to collaborate on making better-informed decisions.”
Such data could be used, for instance, to assess surface water infrastructure investment such as better drainage, given that most properties are at risk from surface water flooding.
