Big Data predicts extreme weather blackspots for UK emergency services

Big Data predicts extreme weather blackspots for UK emergency services
The Visualisation Suite at STFC’s Hartree Centre (Credit: STFC)
The Visualisation Suite at STFC’s Hartree Centre (Credit: STFC)
The Visualisation Suite at STFC’s Hartree Centre
(Credit: STFC)

5 September 2015 – UK start-up company, KnowNow Information Ltd, is revolutionising how UK emergency services can plan for, and respond to, extreme weather conditions, to help save lives and millions of pounds. Using Big Data and the UK’s most powerful supercomputers dedicated to industry, it has developed a capability that can accurately predict the probability of certain types of emergency occurring, based on location and weather conditions.

Torrential rain, overflowing rivers and heavy snowfall – these are just some of the factors that can bring danger and disruption through flooding to households, businesses, road users and those relying on public transport during extreme weather. Statistics show that 32% of flood-related deaths are by drowning in a vehicle, and delaying response to a water rescue from 5 to10 minutes multiplies the risk of fatality by more than 4 times.

KnowNow are using the world-leading big data analysis capabilities of the Science and Technology Facilities Council’s (STFC) Hartree Centre to develop a flood event model that combines existing open data generated by the UK’s emergency services, UK Met Office, Ordnance Survey, the British Geological Survey and the Environment Agency, to name a few, into a single, insightful repository of knowledge that makes such predictions possible.

The company won access to the Hartree Centre as part of a competition run in conjunction with the Open Data Institute. Part of STFC’s Daresbury Laboratory, located at Sci-Tech Daresbury, the Hartree Centre recently announced major Government investment towards its £313 million partnership with IBM to help businesses, such as KnowNow, make the best use of big data and reduce the time and cost of developing new and better products and services.

Knownow’s predictive capability will enable emergency services, highways authorities, rail operators, local communities and businesses to improve their decision making and resource planning when there is a potential for flooding. A public facing ‘app’ will allow local residents to see the risks within their area and insurance companies will also be able to base prices, policies and products on more accurate assessments.

Lee Hannis, Business Development Manager at STFC’s Hartree Centre, said: “The Hartree Centre’s world class analytics skills and visualisation facilities can be accessed by SMEs, to develop their business concepts both quickly and cost effectively. KnowNow is a perfect example of this, achieving a commercially viable tool that will have a positive impact on the UK economy and safety of the public, whether it is used by the emergency services, local authorities and businesses, the general public or insurance providers.”

Through an advanced algorithm developed on the Hartree Centre’s high performance supercomputers, KnowNow’s ‘Flood Event Model’ combines information such as monthly rainfall data, daily river flow, terrain and bedrock classification and emergency call out histories to accurately predict when and where road accidents caused by flooding are likely to occur. Rather than predicting just where flooding will occur, or when, it also predicts a specific kind of incident, such as a car getting stuck in an overflowing river.

David Patterson, Co-Founder & Director, KnowNow Information Ltd, said: “Responding to weather-related emergencies is complex and resource-hungry, and the ability to predict accurately where and when they will occur has huge potential to protect lives, livelihoods and cut costs. Using the supercomputers and expertise at the Hartree Centre, we were able to ‘mash up’ and time-sort all the data which we then overlayed onto Ordnance Survey information, pinpointing the location of key infrastructure, buildings and other assets. The result was a robust platform of evidence highlighting trends and triggers that determine the probability of specific types of emergency occurring in specific places under specific weather conditions.

We are already in talks with local councils and Fire and Rescue Services who are interested to see how they can use our Flood Event Model.”

KnowNow is initially focussing its Flood Event Model on the geographical area of Hampshire and it is intended that the project will be rolled out nationally in the near future.”

Big Data is one of The Government’s Eight Great Technologies to support UK science strengths and business capabilities.


Wendy Ellison
STFC Press Officer
Tel: 01925 603232 / 07919 548012

STFC Hartree Centre

Part of the STFC Daresbury Laboratory, and located within the Sci-Tech Daresbury science and innovation campus, the Hartree Centre is driving growth and innovation between science and industry using intense computing.

Every day, the Hartree Centre collaborates with industrial clients and research partners on projects that create insights and value using high performance computing, Big Data analytics, simulation and modelling. By combining one of the most powerful supercomputers in the world dedicated to industrial engagement with access to data scientists, engineers and software specialists, the Centre is enabling organisations of all sizes to produce better outcomes, products and services more quickly and cost-effectively than they can through conventional R&D workflows.

Underpinned by nearly £170m of Government funding, the Hartree Centre is enabling academics and industry within the UK and internationally to solve research challenges, establish competitive advantage and stimulate economic growth.

In partnerships, the Hartree Centre is also developing the next generation of supercomputing architectures and software, combining existing best practice with innovation to deliver faster, more energy sustainable solutions capable of meeting the challenges of data intensive computing.