2022
DOI: 10.1016/j.hal.2022.102335
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Apparent biogeographical trends in Alexandrium blooms for northern Europe: identifying links to climate change and effective adaptive actions

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Cited by 11 publications
(6 citation statements)
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“…Few machine learning techniques for modeling the probability of toxic algae have been explored, including GLM (Anderson et al, 2011), decision trees (Bouquet et al, 2022), and GBM (Klemm et al, 2022). We demonstrate that SVM is a highly reliable approach for estimating the presence and HA probability of toxic algae in Norwegian coastal waters.…”
Section: Discussionmentioning
confidence: 99%
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“…Few machine learning techniques for modeling the probability of toxic algae have been explored, including GLM (Anderson et al, 2011), decision trees (Bouquet et al, 2022), and GBM (Klemm et al, 2022). We demonstrate that SVM is a highly reliable approach for estimating the presence and HA probability of toxic algae in Norwegian coastal waters.…”
Section: Discussionmentioning
confidence: 99%
“…and Dinophysis spp. are commonly associated with stratified waters (Klemm et al, 2022; Reguera et al, 2012). The simulated response supports these associations, as the presence probability of D. acuminata , Alexandrium spp., and A. tamarense increases in shallower MLD—commonly correlated with more stratified waters.…”
Section: Discussionmentioning
confidence: 99%
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