Drivers of harmful algal blooms in coastal areas of Eastern Mediterranean: a machine learning methodological approach

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 techniq...

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Autores principales:	Androniki Tamvakis, George Tsirtsis, Michael Karydis, Kleanthis Patsidis, Giorgos D. Kokkoris
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	harmful algal machine learning random forest abiotic parameters eastern mediterranean Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/e39e6b3c9c404c61b0571a2509caa1d1
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spelling	oai:doaj.org-article:e39e6b3c9c404c61b0571a2509caa1d12021-11-11T01:35:19ZDrivers of harmful algal blooms in coastal areas of Eastern Mediterranean: a machine learning methodological approach10.3934/mbe.20213221551-0018https://doaj.org/article/e39e6b3c9c404c61b0571a2509caa1d12021-07-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021322?viewType=HTMLhttps://doaj.org/toc/1551-0018Harmful 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 as drivers of blooms. Random Forest (RF) algorithm achieved the best predictive performance by correctly identifying the presence of most genera with a mean of 89.2% 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. The 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 the 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.Androniki TamvakisGeorge TsirtsisMichael KarydisKleanthis PatsidisGiorgos D. KokkorisAIMS Pressarticleharmful algalmachine learningrandom forestabiotic parameterseastern mediterraneanBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6484-6505 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	harmful algal machine learning random forest abiotic parameters eastern mediterranean Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	harmful algal machine learning random forest abiotic parameters eastern mediterranean Biotechnology TP248.13-248.65 Mathematics QA1-939 Androniki Tamvakis George Tsirtsis Michael Karydis Kleanthis Patsidis Giorgos D. Kokkoris Drivers of harmful algal blooms in coastal areas of Eastern Mediterranean: a machine learning methodological approach
description	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 as drivers of blooms. Random Forest (RF) algorithm achieved the best predictive performance by correctly identifying the presence of most genera with a mean of 89.2% 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. The 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 the 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.
format	article
author	Androniki Tamvakis George Tsirtsis Michael Karydis Kleanthis Patsidis Giorgos D. Kokkoris
author_facet	Androniki Tamvakis George Tsirtsis Michael Karydis Kleanthis Patsidis Giorgos D. Kokkoris
author_sort	Androniki Tamvakis
title	Drivers of harmful algal blooms in coastal areas of Eastern Mediterranean: a machine learning methodological approach
title_short	Drivers of harmful algal blooms in coastal areas of Eastern Mediterranean: a machine learning methodological approach
title_full	Drivers of harmful algal blooms in coastal areas of Eastern Mediterranean: a machine learning methodological approach
title_fullStr	Drivers of harmful algal blooms in coastal areas of Eastern Mediterranean: a machine learning methodological approach
title_full_unstemmed	Drivers of harmful algal blooms in coastal areas of Eastern Mediterranean: a machine learning methodological approach
title_sort	drivers of harmful algal blooms in coastal areas of eastern mediterranean: a machine learning methodological approach
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/e39e6b3c9c404c61b0571a2509caa1d1
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Drivers of harmful algal blooms in coastal areas of Eastern Mediterranean: a machine learning methodological approach

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