Improving statistical classification methods and ecological status assessment for river macroinvertebrates
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.
PublisherUniversity of Jyväskylä
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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Raitoharju, Jenni; Riabchenko, Ekaterina; Ahmad, Iftikhar; Iosifidis, Alexandros; Gabbouj, Moncef; Kiranyaz, Serkan; Tirronen, Ville; Ärje, Johanna; Kärkkäinen, Salme; Meissner, Kristian (Elsevier BV, 2018)Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and ...
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