Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Explainable AI Framework for Multivariate Hydrochemical Time Series

Version 1 : Received: 15 November 2020 / Approved: 17 November 2020 / Online: 17 November 2020 (14:01:33 CET)

How to cite: Thrun, M.; Ultsch, A.; Breuer, L. Explainable AI Framework for Multivariate Hydrochemical Time Series. Preprints 2020, 2020110451 (doi: 10.20944/preprints202011.0451.v1). Thrun, M.; Ultsch, A.; Breuer, L. Explainable AI Framework for Multivariate Hydrochemical Time Series. Preprints 2020, 2020110451 (doi: 10.20944/preprints202011.0451.v1).

Abstract

The understanding of water quality and its underlying processes is important for the protection of aquatic environments enabling the rare opportunity of access to a domain expert. Hence, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series resulting in explanations that are interpretable by a domain expert. The XAI combines in three steps a data-driven choice of a distance measure with explainable cluster analysis through supervised decision trees. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The XAI does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two comparable decision-based XAIs were unable to provide meaningful and relevant explanations from the multivariate time series data. Open-source code in R for the three steps of the XAI framework is provided.

Supplementary and Associated Material

https://doi.org/10.5281/zenodo.4274700: Data and Source CODE of XAI

Subject Areas

Explainable AI; Cluster Analysis; Swarm Intelligence; Machine Learning System; High-Dimensional Data Visualization; Decision Trees

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