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Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles

Author

Listed:
  • Andreas Wunsch

    (Institute of Applied Geosciences, Division of Hydrogeology)

  • Tanja Liesch

    (Institute of Applied Geosciences, Division of Hydrogeology)

  • Stefan Broda

    (Federal Institute for Geosciences and Natural Resources (BGR))

Abstract

Hydrograph clustering helps to identify dynamic patterns within aquifers systems, an important foundation of characterizing groundwater systems and their influences, which is necessary to effectively manage groundwater resources. We develope an unsupervised modeling approach to characterize and cluster hydrographs on regional scale according to their dynamics. We apply feature-based clustering to improve the exploitation of heterogeneous datasets, explore the usefulness of existing features and propose new features specifically useful to describe groundwater hydrographs. The clustering itself is based on a powerful combination of Self-Organizing Maps with a modified DS2L-Algorithm, which automatically derives the cluster number but also allows to influence the level of detail of the clustering. We further develop a framework that combines these methods with ensemble modeling, internal cluster validation indices, resampling and consensus voting to finally obtain a robust clustering result and remove arbitrariness from the feature selection process. Further we propose a measure to sort hydrographs within clusters, useful for both interpretability and visualization. We test the framework with weekly data from the Upper Rhine Graben System, using more than 1800 hydrographs from a period of 30 years (1986-2016). The results show that our approach is adaptively capable of identifying homogeneous groups of hydrograph dynamics. The resulting clusters show both spatially known and unknown patterns, some of which correspond clearly to external controlling factors, such as intensive groundwater management in the northern part of the test area. This framework is easily transferable to other regions and, by adapting the describing features, also to other time series-clustering applications.

Suggested Citation

  • Andreas Wunsch & Tanja Liesch & Stefan Broda, 2022. "Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 39-54, January.
  • Handle: RePEc:spr:waterr:v:36:y:2022:i:1:d:10.1007_s11269-021-03006-y
    DOI: 10.1007/s11269-021-03006-y
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    References listed on IDEAS

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    1. P. (Sundar) Balakrishnan & Martha Cooper & Varghese Jacob & Phillip Lewis, 1994. "A study of the classification capabilities of neural networks using unsupervised learning: A comparison withK-means clustering," Psychometrika, Springer;The Psychometric Society, vol. 59(4), pages 509-525, December.
    2. Mangiameli, Paul & Chen, Shaw K. & West, David, 1996. "A comparison of SOM neural network and hierarchical clustering methods," European Journal of Operational Research, Elsevier, vol. 93(2), pages 402-417, September.
    3. Mingoti, Sueli A. & Lima, Joab O., 2006. "Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1742-1759, November.
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    Cited by:

    1. Andreas Wunsch & Tanja Liesch & Stefan Broda, 2022. "Deep learning shows declining groundwater levels in Germany until 2100 due to climate change," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Renee Obringer & Dave D. White, 2023. "Leveraging Unsupervised Learning to Develop a Typology of Residential Water Users’ Attitudes Towards Conservation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 37-53, January.

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