Combining indicators for better decisions – Algorithms vs experts on lakes ecological status assessment

The results of ecological condition assessments of ecosystems are related to key decisions taken for the purpose of remedial measures or maintaining their current state. In the assessment process, experts come across extensive datasets, the quality, and completeness of which do not always allow for...

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Autores principales:	Grzegorz Chrobak, Tomasz Kowalczyk, Thomas B. Fischer, Katarzyna Chrobak, Szymon Szewrański, Jan K. Kazak
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Environmental management Ecological assessment Water quality Ecosystems Neural networks Decision support Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/5c4f1588eb54443994b48fc004934d93
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id	oai:doaj.org-article:5c4f1588eb54443994b48fc004934d93
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spelling	oai:doaj.org-article:5c4f1588eb54443994b48fc004934d932021-12-01T05:02:34ZCombining indicators for better decisions – Algorithms vs experts on lakes ecological status assessment1470-160X10.1016/j.ecolind.2021.108318https://doaj.org/article/5c4f1588eb54443994b48fc004934d932021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21009833https://doaj.org/toc/1470-160XThe results of ecological condition assessments of ecosystems are related to key decisions taken for the purpose of remedial measures or maintaining their current state. In the assessment process, experts come across extensive datasets, the quality, and completeness of which do not always allow for a reliable evaluation, especially if a single empirical approach is used. In this paper, results of machine learning algorithms are presented, with a focus on Self-Organizing Maps. In this context, measurements of component parameters for the assessment of the ecological state of lake ecosystems were subjected to the process of unsupervised machine learning with the aim to create an alternative assessment approach based on the capabilities of neural networks. Results are mapped and compared with expert evaluations, allowing to extend knowledge about sub-clusters present in the data. The primary target of this paper is the ecological assessment expert. At an early stage, information was obtained about the presence of ecological outliers that may be subject to separate monitoring or verification of environmental activities and objectives. In the back-mapping process, the presented technique of map construction and clustering with various versions of the division was referenced to a set of expert classification findings, revealing the underlying structure of the results when addressed with an unsupervised data comprehension. The approach introduced here does not intend to interfere with the format of an original assessment methodology. Rather it aims at obtaining useful additional information which may help in making better decisions.Grzegorz ChrobakTomasz KowalczykThomas B. FischerKatarzyna ChrobakSzymon SzewrańskiJan K. KazakElsevierarticleEnvironmental managementEcological assessmentWater qualityEcosystemsNeural networksDecision supportEcologyQH540-549.5ENEcological Indicators, Vol 132, Iss , Pp 108318- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Environmental management Ecological assessment Water quality Ecosystems Neural networks Decision support Ecology QH540-549.5
spellingShingle	Environmental management Ecological assessment Water quality Ecosystems Neural networks Decision support Ecology QH540-549.5 Grzegorz Chrobak Tomasz Kowalczyk Thomas B. Fischer Katarzyna Chrobak Szymon Szewrański Jan K. Kazak Combining indicators for better decisions – Algorithms vs experts on lakes ecological status assessment
description	The results of ecological condition assessments of ecosystems are related to key decisions taken for the purpose of remedial measures or maintaining their current state. In the assessment process, experts come across extensive datasets, the quality, and completeness of which do not always allow for a reliable evaluation, especially if a single empirical approach is used. In this paper, results of machine learning algorithms are presented, with a focus on Self-Organizing Maps. In this context, measurements of component parameters for the assessment of the ecological state of lake ecosystems were subjected to the process of unsupervised machine learning with the aim to create an alternative assessment approach based on the capabilities of neural networks. Results are mapped and compared with expert evaluations, allowing to extend knowledge about sub-clusters present in the data. The primary target of this paper is the ecological assessment expert. At an early stage, information was obtained about the presence of ecological outliers that may be subject to separate monitoring or verification of environmental activities and objectives. In the back-mapping process, the presented technique of map construction and clustering with various versions of the division was referenced to a set of expert classification findings, revealing the underlying structure of the results when addressed with an unsupervised data comprehension. The approach introduced here does not intend to interfere with the format of an original assessment methodology. Rather it aims at obtaining useful additional information which may help in making better decisions.
format	article
author	Grzegorz Chrobak Tomasz Kowalczyk Thomas B. Fischer Katarzyna Chrobak Szymon Szewrański Jan K. Kazak
author_facet	Grzegorz Chrobak Tomasz Kowalczyk Thomas B. Fischer Katarzyna Chrobak Szymon Szewrański Jan K. Kazak
author_sort	Grzegorz Chrobak
title	Combining indicators for better decisions – Algorithms vs experts on lakes ecological status assessment
title_short	Combining indicators for better decisions – Algorithms vs experts on lakes ecological status assessment
title_full	Combining indicators for better decisions – Algorithms vs experts on lakes ecological status assessment
title_fullStr	Combining indicators for better decisions – Algorithms vs experts on lakes ecological status assessment
title_full_unstemmed	Combining indicators for better decisions – Algorithms vs experts on lakes ecological status assessment
title_sort	combining indicators for better decisions – algorithms vs experts on lakes ecological status assessment
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/5c4f1588eb54443994b48fc004934d93
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Combining indicators for better decisions – Algorithms vs experts on lakes ecological status assessment

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