Improving statistical classification methods and ecological status assessment for river macroinvertebrates
Abstract
Aquatic ecosystems are facing a growing number of human-induced stressors and the need to
implement more biomonitoring to assess the ecological status of water bodies is eminent. This dissertation aims at providing tools to reduce the costs and improve the accuracy of freshwater benthic macroinvertebrate biomonitoring. To improve the cost-e ciency, we consider automated classi cation and develop a novel classi er suitable for complex macroinvertebrate image data. To enhance the accuracy of macroinvertebrate biomonitoring, we study the statistical properties of the Percent Model A nity index crucial to current Finnish biomonitoring and the factors a ecting these statistics. Finally, we perform a simulation study to analyze how di erent biological indices are a ected by misclassi cations in automated identi cation of macroinvertebrates.
Main Author
Format
Theses
Doctoral thesis
Published
2016
Series
Subjects
ISBN
978-951-39-6707-9
Publisher
University of Jyväskylä
The permanent address of the publication
https://urn.fi/URN:ISBN:978-951-39-6707-9Käytä tätä linkitykseen.
ISSN
1457-8905
Language
English
Published in
Report / University of Jyväskylä. Department of Mathematics and Statistics