Ecological state evaluation of lake ecosystems revisited: Latent variables with kSVM algorithm approach for assessment automatization and data comprehension

Automated and reproducible methodology for assessing the ecological condition of lakes is essential for effective monitoring and facilitating the decision-making process aimed at achieving the stated environmental goals. At the same time, multidimensional measurement datasets are often an obstacle t...

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Autores principales:	Grzegorz Chrobak, Tomasz Kowalczyk, Thomas B. Fischer, Szymon Szewrański, Katarzyna Chrobak, Jan K. Kazak
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Ecological assessment Lake ecosystems Machine learning Latent variable analysis Ecosystems Decision support Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/881a08d3731b477690e63db79963d41c
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spelling	oai:doaj.org-article:881a08d3731b477690e63db79963d41c2021-12-01T04:48:15ZEcological state evaluation of lake ecosystems revisited: Latent variables with kSVM algorithm approach for assessment automatization and data comprehension1470-160X10.1016/j.ecolind.2021.107567https://doaj.org/article/881a08d3731b477690e63db79963d41c2021-06-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21002326https://doaj.org/toc/1470-160XAutomated and reproducible methodology for assessing the ecological condition of lakes is essential for effective monitoring and facilitating the decision-making process aimed at achieving the stated environmental goals. At the same time, multidimensional measurement datasets are often an obstacle to drawing insightful conclusions, thus becoming an incentive for overly simplified analyzes. In this article, a set of measurements and ecological status assessment results for a collection of 499 lakes in Poland was used. Expert assessment process was recreated using the supervised kernel Support Vector Machine algorithm on dataset with reduced dimensionality, thus a model that automates the ecological assessment process was obtained. The use of the explanatory skill of latent variables made it possible to present the assessed objects along with their position in individual classes. The visualization of the results in reduced dimensionality increased, without interfering with the size of the classes, the informative evaluation potential, which should be considered as an acompanying assessment parameter in the future. The primary target of this paper is the ecological expert coping with automatization of assessment process and obtaining latent information for sense-making visual comprehension during consultations regarding ecosystem-oriented ecological decision making.Grzegorz ChrobakTomasz KowalczykThomas B. FischerSzymon SzewrańskiKatarzyna ChrobakJan K. KazakElsevierarticleEcological assessmentLake ecosystemsMachine learningLatent variable analysisEcosystemsDecision supportEcologyQH540-549.5ENEcological Indicators, Vol 125, Iss , Pp 107567- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ecological assessment Lake ecosystems Machine learning Latent variable analysis Ecosystems Decision support Ecology QH540-549.5
spellingShingle	Ecological assessment Lake ecosystems Machine learning Latent variable analysis Ecosystems Decision support Ecology QH540-549.5 Grzegorz Chrobak Tomasz Kowalczyk Thomas B. Fischer Szymon Szewrański Katarzyna Chrobak Jan K. Kazak Ecological state evaluation of lake ecosystems revisited: Latent variables with kSVM algorithm approach for assessment automatization and data comprehension
description	Automated and reproducible methodology for assessing the ecological condition of lakes is essential for effective monitoring and facilitating the decision-making process aimed at achieving the stated environmental goals. At the same time, multidimensional measurement datasets are often an obstacle to drawing insightful conclusions, thus becoming an incentive for overly simplified analyzes. In this article, a set of measurements and ecological status assessment results for a collection of 499 lakes in Poland was used. Expert assessment process was recreated using the supervised kernel Support Vector Machine algorithm on dataset with reduced dimensionality, thus a model that automates the ecological assessment process was obtained. The use of the explanatory skill of latent variables made it possible to present the assessed objects along with their position in individual classes. The visualization of the results in reduced dimensionality increased, without interfering with the size of the classes, the informative evaluation potential, which should be considered as an acompanying assessment parameter in the future. The primary target of this paper is the ecological expert coping with automatization of assessment process and obtaining latent information for sense-making visual comprehension during consultations regarding ecosystem-oriented ecological decision making.
format	article
author	Grzegorz Chrobak Tomasz Kowalczyk Thomas B. Fischer Szymon Szewrański Katarzyna Chrobak Jan K. Kazak
author_facet	Grzegorz Chrobak Tomasz Kowalczyk Thomas B. Fischer Szymon Szewrański Katarzyna Chrobak Jan K. Kazak
author_sort	Grzegorz Chrobak
title	Ecological state evaluation of lake ecosystems revisited: Latent variables with kSVM algorithm approach for assessment automatization and data comprehension
title_short	Ecological state evaluation of lake ecosystems revisited: Latent variables with kSVM algorithm approach for assessment automatization and data comprehension
title_full	Ecological state evaluation of lake ecosystems revisited: Latent variables with kSVM algorithm approach for assessment automatization and data comprehension
title_fullStr	Ecological state evaluation of lake ecosystems revisited: Latent variables with kSVM algorithm approach for assessment automatization and data comprehension
title_full_unstemmed	Ecological state evaluation of lake ecosystems revisited: Latent variables with kSVM algorithm approach for assessment automatization and data comprehension
title_sort	ecological state evaluation of lake ecosystems revisited: latent variables with ksvm algorithm approach for assessment automatization and data comprehension
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/881a08d3731b477690e63db79963d41c
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Ecological state evaluation of lake ecosystems revisited: Latent variables with kSVM algorithm approach for assessment automatization and data comprehension

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