Preprints
https://doi.org/10.5194/gmd-2020-87
https://doi.org/10.5194/gmd-2020-87
Submitted as: model description paper
 | 
07 May 2020
Submitted as: model description paper |  | 07 May 2020
Status: this preprint was under review for the journal GMD but the revision was not accepted.

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

Michael C. Thrun, Alfred Ultsch, and 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.

Michael C. Thrun, Alfred Ultsch, and Lutz Breuer
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Michael C. Thrun, Alfred Ultsch, and Lutz Breuer

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, Alfred Ultsch, and Lutz Breuer

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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.