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.
Skip Nav Destination
Article navigation
Research Article|
February 19 2016
Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms
C. Stoica;
1National Research and Development Institute for Industrial Ecology-ECOIND, 71-73 Drumul Podu Dambovitei, 060652 Bucharest, Romania
E-mail: [email protected]
Search for other works by this author on:
J. Camejo;
J. Camejo
2Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Search for other works by this author on:
A. Banciu;
A. Banciu
1National Research and Development Institute for Industrial Ecology-ECOIND, 71-73 Drumul Podu Dambovitei, 060652 Bucharest, Romania
Search for other works by this author on:
M. Nita-Lazar;
M. Nita-Lazar
1National Research and Development Institute for Industrial Ecology-ECOIND, 71-73 Drumul Podu Dambovitei, 060652 Bucharest, Romania
Search for other works by this author on:
I. Paun;
I. Paun
1National Research and Development Institute for Industrial Ecology-ECOIND, 71-73 Drumul Podu Dambovitei, 060652 Bucharest, Romania
Search for other works by this author on:
S. Cristofor;
S. Cristofor
3Department of Systemic Ecology and Sustainability, Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, Bucharest, Romania
Search for other works by this author on:
O. R. Pacheco;
O. R. Pacheco
2Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Search for other works by this author on:
M. Guevara
M. Guevara
4Computer Graphics Center, University of Minho, Campus de Azurem, 4800-058 Guimarães, Portugal
Search for other works by this author on:
Water Sci Technol (2016) 73 (10): 2413–2421.
Article history
Received:
October 06 2015
Accepted:
February 05 2016
Citation
C. Stoica, J. Camejo, A. Banciu, M. Nita-Lazar, I. Paun, S. Cristofor, O. R. Pacheco, M. Guevara; Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms. Water Sci Technol 18 May 2016; 73 (10): 2413–2421. doi: https://doi.org/10.2166/wst.2016.097
Download citation file:
Sign in
Don't already have an account? Register
Client Account
You could not be signed in. Please check your email address / username and password and try again.
Could not validate captcha. Please try again.
eBook
Pay-Per-View Access
$38.00