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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-87
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-87
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 07 May 2020

Submitted as: model description paper | 07 May 2020

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A revised version of this preprint is currently under review for the journal GMD.

Explainable AI for Knowledge Acquisition in Hydrochemical Time Series V1.0.0

Michael C. Thrun1, Alfred Ultsch1, and Lutz Breuer2 Michael C. Thrun et al.
  • 1Databionics Research Group, University of Marburg, Germany
  • 2Institute for Landscape Ecology and Resources Management (ILR), Justus Liebig University Giessen

Abstract. The understanding of water quality and its underlying processes is important for the protection of aquatic environments. Here an explainable AI (XAI) based multivariate time series analytical framework is applied on high-frequency water quality measurements including nitrate and electrical conductivity and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by a cluster analysis which does not depend on prior knowledge about data structure. The cluster analysis is designed to find similar days within a cluster and dissimilar days between clusters. This allows for the data-driven choice of a distance measure. Using a swarm based AI system, the resulting cluster define three states of water bodies, which can be visualized by a topographic map of high-dimensional structures. These structures are explained by rules extracted from decision trees. The rules generated by the XAI system improve the understanding of aquatic environments. The model description presented here allows to extract meaningful, useful, and new knowledge from multivariate time series.

Michael C. Thrun et al.

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Michael C. Thrun et al.

Data sets

Datasets of ExplainableAI4KnowledgeAcquisitionStreamTS2020: Analytic Procedure for Explainable AI for Knowledge Acquisition in Hydrochemical Time Series M. Thrun and A. Ultsch https://doi.org/10.5281/zenodo.3734892

Model code and software

Databionic Swarm M. Thrun https://doi.org/10.5281/zenodo.3786218

Executable research compendia (ERC)

ExplainableAI4KnowledgeAcquisitionStreamTS2020: Analytic Procedure for Explainable AI for Knowledge Acquisition in Hydrochemical Time Series M. Thrun and A. Ultsch https://doi.org/10.5281/zenodo.3734892

Michael C. Thrun et al.

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Latest update: 28 Sep 2020
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Short summary
We propose an explainable AI (XAI) framework for times series describing water quality & environmental parameters. The relationship between parameters is investigated by swarm based cluster analysis designed to find similar days within & dissimilar days between clusters. Resulting clusters define three states of water bodies & are visualized by a topographic map of high-dimensional structures. Rules generated by the XAI system explain clusters & improve the understanding of aquatic environments.
We propose an explainable AI (XAI) framework for times series describing water quality &...
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