The number of sources where data are freely available is growing fast. This means that huge amounts of increasingly complex information are now available. Digitalisation is about automating and simplifying data use.
NILU has extensive experience in handling complex data structures, including data collection, storage and visualisation. This enables us to utilise advanced and scalable systems, including machine learning technology, to automate processes and generate new knowledge, which in turn enables NILU to provide innovative data services. The most recent advance in this field is to use low-cost sensors to measure air quality, in combination with machine learning, to automate calibration and quality control.
The implementation of machine learning enables new methods for calibration and verification of monitored data, which in turn provide new knowledge and information that can be used as basis for decision-making or information to the public.
Big Data is information characterised by high volume, high speed and/or a high degree of variation. Processing and analysing these large and complex data sets efficiently requires capabilities and technology beyond those offered by traditional IT systems.
Machine Learning uses statistical techniques to help us understand and predict events and outcomes based on large and complex data sets.
At NILU we both use and develop advanced machine learning technology to, among other things, complement existing knowledge of models and measurement techniques. Machine learning is also an important tool in NILU’s work on predicting air quality.
Visualisation of data is important in order to be able to develop complete solutions, including analysis and dissemination of results.
NILU develops solutions for the internet, apps and other mobile tools, making it easy to collect, share and disseminate air quality data.