2020 INSPIRE hackathon D3

What: The weather is a chaotic system. Small errors in the initial conditions of a forecast grow rapidly, and affect predictability. Furthermore, predictability is limited by model errors due to the approximate simulation of atmospheric processes of the stateof- the-art numerical models. These days, on the other hand, we gather a lot of data thanks to modern IoT technologies richly occupied in the fields. Incorporation of global weather data among data collected from sensors in the same field can contribute to accurate weather forecast in the local environment. Preparation of training data i.e. creation of preprocessing pipeline where the global weather data would be correctly enhanced by in local data from sensors. Using RNNs (LSTM) with supporting Vowpal Wabbit or Prophet.

Why: Adaptation of deep learning algorithms specialized for time-series prediction can be beneficial or more accurate for weather forecasting in the local environment for farmers than the publicly available global forecast model.

Who (is the webinar for): Researchers who are interested in time-series and high-dimensional function modelling/prediction via deep learning.

Ondrej Kaas was born in 1991, currently researching doctorate in the area of clustering algorithms at Computer Science at the University of West Bohemia, Faculty of Applied Science in Pilsen, Czech Republic. Employed history contains more than six years as a machine learning researcher of big data in-car telemetry, internet advertisement, and remote sensing of Earth. His professionalism of interest is time series prediction, object detection, and semantic segmentation in satellite data based on deep neural networks and related distributed computing. His technical skills cover the development of real-time cloud-based applications, IoT, and sensors.

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