A comparison of classification methods in automated taxa identification of benthic macroinvertebrates. (2017)

• Record Type: Journal Article
• Authors: Joutsijoki, Henry; Juhola, Martti
• Is Part Of: International journal of data science. Volume 2:Number 4(2017)
• Page Start: 273
• Page End: 300
• Publication Date: 2017
• Subjects: benthic macroinvertebrates -- classification -- machine learning -- water quality; Big data -- Periodicals; Data mining -- Periodicals; Information storage and retrieval systems -- Periodicals; Decision making -- Data processing -- Periodicals; 005.7
• Languages: English
• ISSNs: 2053-0811

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.