1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
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Τύπος:	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	Drivers of harmful algal blooms in coastal areas of Eastern Mediterranean: a machine learning methodological approach
Συγγραφέας:	[EL] Ταμβάκη, Ανδρονίκη[EN] Tamvaki, Androniki [EL] Τσιρτσής, Γεώργιος[EN] Tsirtsis, George [EL] Καρύδης, Μιχαήλ[EN] Karydis, Michael [EL] Πατσίδης, Κλεάνθης[EN] Patsidis, Kleanthis [EL] Κόκκορης, Γεώργιος[EN] Kokkoris, Georgios
Ημερομηνία:	28/07/2021
Περίληψη:	Harmful algal species are present in the Mediterranean Sea and are often associated with toxic events affecting the nearby coastal zones. The presence of 18 marine microalgae, at genus level, associated with potentially harmful characteristics was predicted using a number of machine learning techniques based exclusively on a small set of abiotic variables, already identified a s drivers of blooms. Random Forest (RF) algorithm achieved the best predictive performance by correctly identifying the presence of most genera with a mean of 8 9 2 % of total samples. Although, RF has shown lower predictive performance for genera present in a low number of samples, its predictive power remains at least “fair’ in these cases. T he main tree based advantage of RF was thereafter used to assess the importance of the input variables in predicting the presence of the algal genera. Temperature had t he most powerful effect on genera’s presences, although this effect varies among genera. Finally, the genera were clustered based on their response to the considered abiotic variables and common trends in an ecological context were identified.
Γλώσσα:	Αγγλικά
Σελίδες:	22
DOI:	10.3934/mbe.2021322
Θεματική κατηγορία:	[EL] Θαλάσσια βιολογία και Βιολογία γλυκών υδάτων[EN] Marine and Freshwater Biology
Λέξεις-κλειδιά:	Harmful algal; Machine learning; Random Forest; Abiotic parameters; Eastern Mediterranean
Κάτοχος πνευματικών δικαιωμάτων:	©2021 the Author(s), licensee AIMS Press
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://www.aimspress.com/article/doi/10.3934/mbe.2021322
Ηλεκτρονική διεύθυνση περιοδικού:	https://www.aimspress.com/journal/MBE
Τίτλος πηγής δημοσίευσης:	Mathematical Biosciences and Engineering
Τεύχος:	5
Τόμος:	18
Σελίδες τεκμηρίου (στην πηγή):	6484-6505
Σημειώσεις:	This research is funded in the context of the project “Predicting harmful algae blooms using machine learning techniques” (MIS 5047187) under the call for proposals “Supporting researchers with an emphasis on new researchers” (EDULLL 103). The project is co-financed by Greece and the European Union (European Social Fund- ESF) by the Operational Programme Human Resources Development, Education and Lifelong Learning 2014–2020.
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