Abstract
Biological methods of monitoring river water quality have enormous potential but this is not presently being realised owing to inadequacies in methods of data interpretation and classification. This paper describes the development and testing of several classification models based on Bayesian, neural and machine learning techniques, and compares their performance with two traditional models. It is demonstrated, using an expertly classified test data set, that ‘naive’ Bayesian models and multi-layered perceptrons can significantly out-perform the traditional methods. It is concluded that these two techniques presently provide the most promising means of realising the full potential of bio-monitoring, either acting separately or jointly as complementary ’experts’.
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© 1996 Springer Science+Business Media Dordrecht
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Walley, W.J., Džeroski, S. (1996). Biological Monitoring: a Comparison between Bayesian, Neural and Machine Learning Methods of Water Quality Classification. In: Denzer, R., Schimak, G., Russell, D. (eds) Environmental Software Systems. IFIP — The International Federation for Information Processing. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-34951-0_20
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DOI: https://doi.org/10.1007/978-0-387-34951-0_20
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