Title: A comparison of classification methods in automated taxa identification of benthic macroinvertebrates

Authors: Henry Joutsijoki; Martti Juhola

Addresses: School of Information Sciences, University of Tampere, Kanslerinrinne 1, FI-33014, Tampere, Finland ' School of Information Sciences, University of Tampere, Kanslerinrinne 1, FI-33014, Tampere, Finland

Abstract: In this research, we examined the automated taxa identification of benthic macroinvertebrates. Benthic macroinvertebrates play an important role in biomonitoring. They can be used in water quality assessments. Identification of benthic macroinvertebrates is made usually by highly trained experts, but this approach has high costs and, hence, the automation of this identification process could reduce the costs and would make wider biomonitoring possible. The automated taxa identification of benthic macroinvertebrates returns to image classification. We applied altogether 11 different classification methods to the image dataset of eight taxonomic groups of benthic macroinvertebrates. Wide experimental tests were performed. The best results, around 94% accuracies, were achieved when quadratic discriminant analysis (QDA), radial basis function network and multi-layer perceptron (MLP) were used. On the basis of the results, it can be said that the automated taxa identification of benthic macroinvertebrates is possible with high accuracy.

Keywords: benthic macroinvertebrates; classification; machine learning; water quality.

DOI: 10.1504/IJDS.2017.088101

International Journal of Data Science, 2017 Vol.2 No.4, pp.273 - 300

Received: 13 Mar 2015
Accepted: 23 Sep 2015
Published online: 24 Nov 2017 *

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