Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes

dc.creator	Mellios, Nikolaos	en
dc.creator	Moe, Jannicke	en
dc.creator	Laspidou, Chrysi	en
dc.date.accessioned	2020-11-03T10:55:59Z
dc.date.available	2020-11-03T10:55:59Z
dc.date.issued	2020-04-22
dc.identifier	https://doi.org/10.3390/w12041191
dc.identifier.uri	http://hdl.handle.net/11615/54407
dc.language.iso	en	en
dc.relation	European Union Horizon 2020 Research and Innovation Staff Exchange programme under Grant Agreement No. 734409 WATER4CITIES	en
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	en
dc.source	Multidisciplinary Digital Publishing Institute	en
dc.subject	cyanobacteria blooms	en
dc.subject	eutrophication	en
dc.subject	lakes monitoring data	en
dc.subject	path analysis	en
dc.subject	machine learning algorithms	en
dc.subject	Decision Tree	en
dc.subject	K-Nearest Neighbor	en
dc.subject	Support-vector Machine	en
dc.subject	Random Forest	en
dc.subject	model validation	en
dc.title	Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes	en
dc.type	journalArticle	en
dc.rights.accessRights	free	en
dc.identifier.bibliographicCitation	Mellios, N.; Moe, S.J.; Laspidou, C. Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes. Water 2020, 12, 1191.	en

Attribution-NonCommercial-NoDerivatives 4.0 International