Water quality and macrophytes in the Danube River: Artificial neural network modelling

Ecological assessment of large rivers such as the Danube is a challenging task. Eutrophication was reported as one of the main drivers that structure aquatic communities in the Danube basin. Due to their sedentary nature, relatively slow growth/ long life spans, and engineering role in aquatic ecosy...

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Autores principales:	Ivana Krtolica, Dušanka Cvijanović, Đorđe Obradović, Maja Novković, Djuradj Milošević, Dragan Savić, Mirjana Vojinović-Miloradov, Snežana Radulović
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	ANN Aquatic vegetation Eutrophication Joint Danube Survey Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/19e011dc6f33476e963be66359740820
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spelling	oai:doaj.org-article:19e011dc6f33476e963be663597408202021-12-01T04:34:21ZWater quality and macrophytes in the Danube River: Artificial neural network modelling1470-160X10.1016/j.ecolind.2020.107076https://doaj.org/article/19e011dc6f33476e963be663597408202021-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X20310153https://doaj.org/toc/1470-160XEcological assessment of large rivers such as the Danube is a challenging task. Eutrophication was reported as one of the main drivers that structure aquatic communities in the Danube basin. Due to their sedentary nature, relatively slow growth/ long life spans, and engineering role in aquatic ecosystems, macrophytes are widely used in the detection of nutrient enrichment. In this study, macrophyte presence-absence data within the 3 km long reaches obtained from the Joint Danube Survey (JDS3) were used to predict the water quality of the Danube river and its main tributaries. For each water quality variable (dissolved oxygen, nitrate-nitrogen, and orthophosphates), a multi-layer feed-forward artificial neural network model (ANN) was constructed using the macrophytes as explanatory variables. Despite the limited number of samples (123) along the wide trophic gradient of the Danube, the model showed good predictive performances for the main river channel. The highest discrepancy between observed and predicted water quality was obtained for the samples collected in the tributaries or downstream from the tributaries' mouth, where the model predicted better trophic conditions compared to measured ones. From 64 analysed macrophyte species, 28 were selected by sensitivity analysis as key water quality indicators (KIS) for at least one environmental variable. KIS mainly belonged to the eutrophic tolerant submerged or emerged species with broad ecological amplitude, which reflects the significance of the developed model for use on rivers subjected to nutrient pollution. However, the use of the developed predictive model is restricted to the river sections having a water velocity suitable for macrophytes growth. The developed ANN architecture represents the modelling approach which could be applied to other lotic systems and biological quality elements.Ivana KrtolicaDušanka CvijanovićĐorđe ObradovićMaja NovkovićDjuradj MiloševićDragan SavićMirjana Vojinović-MiloradovSnežana RadulovićElsevierarticleANNAquatic vegetationEutrophicationJoint Danube SurveyEcologyQH540-549.5ENEcological Indicators, Vol 121, Iss , Pp 107076- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ANN Aquatic vegetation Eutrophication Joint Danube Survey Ecology QH540-549.5
spellingShingle	ANN Aquatic vegetation Eutrophication Joint Danube Survey Ecology QH540-549.5 Ivana Krtolica Dušanka Cvijanović Đorđe Obradović Maja Novković Djuradj Milošević Dragan Savić Mirjana Vojinović-Miloradov Snežana Radulović Water quality and macrophytes in the Danube River: Artificial neural network modelling
description	Ecological assessment of large rivers such as the Danube is a challenging task. Eutrophication was reported as one of the main drivers that structure aquatic communities in the Danube basin. Due to their sedentary nature, relatively slow growth/ long life spans, and engineering role in aquatic ecosystems, macrophytes are widely used in the detection of nutrient enrichment. In this study, macrophyte presence-absence data within the 3 km long reaches obtained from the Joint Danube Survey (JDS3) were used to predict the water quality of the Danube river and its main tributaries. For each water quality variable (dissolved oxygen, nitrate-nitrogen, and orthophosphates), a multi-layer feed-forward artificial neural network model (ANN) was constructed using the macrophytes as explanatory variables. Despite the limited number of samples (123) along the wide trophic gradient of the Danube, the model showed good predictive performances for the main river channel. The highest discrepancy between observed and predicted water quality was obtained for the samples collected in the tributaries or downstream from the tributaries' mouth, where the model predicted better trophic conditions compared to measured ones. From 64 analysed macrophyte species, 28 were selected by sensitivity analysis as key water quality indicators (KIS) for at least one environmental variable. KIS mainly belonged to the eutrophic tolerant submerged or emerged species with broad ecological amplitude, which reflects the significance of the developed model for use on rivers subjected to nutrient pollution. However, the use of the developed predictive model is restricted to the river sections having a water velocity suitable for macrophytes growth. The developed ANN architecture represents the modelling approach which could be applied to other lotic systems and biological quality elements.
format	article
author	Ivana Krtolica Dušanka Cvijanović Đorđe Obradović Maja Novković Djuradj Milošević Dragan Savić Mirjana Vojinović-Miloradov Snežana Radulović
author_facet	Ivana Krtolica Dušanka Cvijanović Đorđe Obradović Maja Novković Djuradj Milošević Dragan Savić Mirjana Vojinović-Miloradov Snežana Radulović
author_sort	Ivana Krtolica
title	Water quality and macrophytes in the Danube River: Artificial neural network modelling
title_short	Water quality and macrophytes in the Danube River: Artificial neural network modelling
title_full	Water quality and macrophytes in the Danube River: Artificial neural network modelling
title_fullStr	Water quality and macrophytes in the Danube River: Artificial neural network modelling
title_full_unstemmed	Water quality and macrophytes in the Danube River: Artificial neural network modelling
title_sort	water quality and macrophytes in the danube river: artificial neural network modelling
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/19e011dc6f33476e963be66359740820
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Water quality and macrophytes in the Danube River: Artificial neural network modelling

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