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Prof. Dr. S. Decker
RWTH Aachen
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D-52056 Aachen
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Performing Distributed Analytics on Decentralised Geo- and Hydrological Data

Thesis type
  • Bachelor
  • Master
Status Finished
Submitted in 2021

In times of increasing weather extremes, weather forecasts and early warning systems for extreme weathers have gained great promise to prevent property damage or to mitigate the risk to the civilian population. Current approaches are based on different data types, such as geo basic data, weather forecasts or other hydrological data, in several data sources (Salas et al., 2020; Kreklow et al., 2020; Hofmann and Schüttrumpf, 2019) However, these approaches rely on data centralisation, which can pose several challenges. First, the centralisation process itself could act as a bottleneck since vast amounts of spatiotemporal data from multiple data sources are transmitted to one single location. Second, the data has to be harmonised locally such that lightweight data analysis tasks or even data model training can be conducted. Lastly, depending on the data type, well-known data privacy regulations hinder institutions to share their data. One solution to circumvent these immanent problems of data centralisation could be Distributed Analytics (DA) approaches. DA poses a paradigm shift to address the mentioned challenges by bringing the analysis algorithm to the data instead of vice versa. By design, data stays within institutional borders and the data owner keeps the sovereignty over the data.

This Master Thesis focuses on the development of an information and DA system for the combination and harmonisation of decentralised geo- and hydrological data. In the first step, well-established and relevant data sources should be discovered. Then, these data sources should be (decentrally) harmonised such that the data can be analysed. This Thesis represents a feasibility study showing that DA could be an alternative to common data centralisation approaches in this domain. This Thesis also covers the conduct of DA use cases such as the collection of simple statistics and multi-modal model applications (E.g. Prediction modelling or model-based simulation).

If you are interested in this thesis, a related topic or have additional questions, please do not hesitate to send a message to, or .


  1. Hofmann, J., Schüttrumpf, H., 2019. Risk-Based Early Warning System for Pluvial Flash Floods: Approaches and Foundations. Geosciences 9 (3), 127.
  2. Kreklow, J., Tetzlaff, B., Burkhard, B., Kuhnt, G., 2020. Radar-Based Precipitation Climatology in Germany—Developments, Uncertainties and Potentials. Atmosphere 11 (2), 217.
  3. Salas, D., Liang, X., Navarro, M., Liang, Y., Luna, D., 2020. An open-data open-model framework for hydrological models’ integration, evaluation and application. Environmental Modelling & Software 126 (3 SPEC. ISS), 104622.

For more information, see the following attachment:

Performing Distributed Analytics on Decentralised Geo- and Hydrological Data_final.pdf — PDF document, 402Kb


Foundational Knowledge: Machine Learning, Deep Learning, Statistics

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