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Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms.

Identifieur interne : 000004 ( PubMed/Corpus ); précédent : 000003; suivant : 000005

Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms.

Auteurs : C. Stoica ; J. Camejo ; A. Banciu ; M. Nita-Lazar ; I. Paun ; S. Cristofor ; O R Pacheco ; M. Guevara

Source :

Water science and technology : a journal of the International Association on Water Pollution Research [ 0273-1223 ] ; 2016.

RBID : pubmed:27191562

Abstract

Environmental issues have a worldwide impact on water bodies, including the Danube Delta, the largest European wetland. The Water Framework Directive (2000/60/EC) implementation operates toward solving environmental issues from European and national level. As a consequence, the water quality and the biocenosis structure was altered, especially the composition of the macro invertebrate community which is closely related to habitat and substrate heterogeneity. This study aims to assess the ecological status of Southern Branch of the Danube Delta, Saint Gheorghe, using benthic fauna and a computational method as an alternative for monitoring the water quality in real time. The analysis of spatial and temporal variability of unicriterial and multicriterial indices were used to assess the current status of aquatic systems. In addition, chemical status was characterized. Coliform bacteria and several chemical parameters were used to feed machine-learning (ML) algorithms to simulate a real-time classification method. Overall, the assessment of the water bodies indicated a moderate ecological status based on the biological quality elements or a good ecological status based on chemical and ML algorithms criteria.

DOI: 10.2166/wst.2016.097
PubMed: 27191562

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pubmed:27191562

Le document en format XML

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<front><div type="abstract" xml:lang="en">Environmental issues have a worldwide impact on water bodies, including the Danube Delta, the largest European wetland. The Water Framework Directive (2000/60/EC) implementation operates toward solving environmental issues from European and national level. As a consequence, the water quality and the biocenosis structure was altered, especially the composition of the macro invertebrate community which is closely related to habitat and substrate heterogeneity. This study aims to assess the ecological status of Southern Branch of the Danube Delta, Saint Gheorghe, using benthic fauna and a computational method as an alternative for monitoring the water quality in real time. The analysis of spatial and temporal variability of unicriterial and multicriterial indices were used to assess the current status of aquatic systems. In addition, chemical status was characterized. Coliform bacteria and several chemical parameters were used to feed machine-learning (ML) algorithms to simulate a real-time classification method. Overall, the assessment of the water bodies indicated a moderate ecological status based on the biological quality elements or a good ecological status based on chemical and ML algorithms criteria.</div>
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Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms.

Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms.

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