Improving statistical classification methods and ecological status assessment for river macroinvertebrates
Julkaistu sarjassa
Report / University of Jyväskylä. Department of Mathematics and StatisticsTekijät
Päivämäärä
2016Oppiaine
TilastotiedeAquatic 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.
Julkaisija
University of JyväskyläISBN
978-951-39-6707-9ISSN Hae Julkaisufoorumista
1457-8905Asiasanat
biomonitorointi tieteellinen luokittelu dimensioiden kirous aquatic ecosysytems biomonitoring classification curse of dimensionality error propagation benthic macroinvertebrates biological indices vesiekosysteemit ekologinen tila pohjaeläimistö selkärangattomat monitorointi automaatio lajinmääritys tilastomenetelmät bayesilainen menetelmä indeksit virheet virheanalyysi
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