EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Hydrochemical Classification of Groundwater with Artificial Neural Networks

Valentin Haselbeck1, Jannes Kordilla2, Florian Krause1, and Martin Sauter2
Valentin Haselbeck et al.
  • 1K+S Aktiengesellschaft, Hydro-/Environmental Geology, Germany (
  • 2Georg-August Universität Göttingen, Geowissenschaftliches Zentrum, Abteilung Angewandte Geologie

Growing datasets of inorganic hydrochemical analyses together with large differences in the measured concentrations raise the demand for data compression while maintaining critical information. The data should subsequently be displayed in an orderly and understandable way. Here, a type of artificial neural network, Kohonen’s self-organizing map (SOM), is trained on inorganic hydrochemical data. Based on this network, clusters are built and associated to the salinity source distribution of the spatial variation at a former potash mining site. This combined two-step clustering approach managed to assign the groundwater analyses automatically to five different clusters, three geogenic and two anthropogenic, according to their inorganic chemical composition. The spatial distribution of the SOM clusters helps to understand the large scale hydrogeological context. This approach provides the hydrogeologist with a tool to quickly and automatically analyze large datasets and present them in a clear and comprehensible format.

How to cite: Haselbeck, V., Kordilla, J., Krause, F., and Sauter, M.: Hydrochemical Classification of Groundwater with Artificial Neural Networks, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22148,, 2020