the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Explainable AI for Knowledge Acquisition in Hydrochemical Time Series V1.0.0
Michael C. Thrun
Alfred Ultsch
Lutz Breuer
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.
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Michael C. Thrun et al.


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RC1: 'Comments', Anonymous Referee #1, 14 Jun 2020
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RC2: 'Reviewer Comments', Anonymous Referee #2, 01 Jul 2020
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AC1: 'Rebuttal to the Comments of both Reviewers', Michael Thrun, 31 Jul 2020


-
RC1: 'Comments', Anonymous Referee #1, 14 Jun 2020
-
RC2: 'Reviewer Comments', Anonymous Referee #2, 01 Jul 2020
-
AC1: 'Rebuttal to the Comments of both Reviewers', Michael Thrun, 31 Jul 2020
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
Interactive computing environment
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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