Neural networks are powerful tools that could explore the basic structure of environmental data. In this work, the most common artificial neural network (ANN) architectures, multi-layer perceptrons (MLPs), radial basis function (RBF) and Kohonen's self-organizing maps (SOM), are applied in order to assess the quality of the water reservoirs used for the domestic and industrial water supply of the city of Athens, Greece. In parallel, ANN models are optimized and their recognition and predictive accuracy is tested. The data set consisted of 89 samples collected from the three Athenian water reservoirs during a period of 6 months (October 2006 to April 2007). Thirteen metals and metalloids, Fe, B, Al, V, Cr, Mn, Ni, Cu, Zn, As, Cd, Ba, Pb, were determined. For the validation of the optimized ANN models, new data from subsequent sampling campaigns (December 2007) were used. The constructed classification models predicted successfully the origin of the new posterior samples and simultaneously revealed the differences in sample compositions that occurred in that period. Critical comparison of the different architectures in site classification and modeling verified the validity and usefulness of ANNs, as a powerful and effective tool for water quality assessment.
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Research Article|
August 01 2013
Comparative use of artificial neural networks for the quality assessment of the water reservoirs of Athens
Eleni G. Farmaki;
Eleni G. Farmaki
1Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771 Athens, Greece
2Athens Water Supply and Sewerage Company (EYDAP SA), Quality Control Division, Acharnes Attikis, Greece
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Nikolaos S. Thomaidis;
1Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771 Athens, Greece
E-mail: ntho@chem.uoa.gr
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Vasil Simeonov;
Vasil Simeonov
3Chair of Analytical Chemistry, Faculty of Chemistry, University of Sofia ‘St. Kl. Okhridski’, J. Bourchier Blvd 1, Sofia 1164, Bulgaria
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Constantinos E. Efstathiou
Constantinos E. Efstathiou
1Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771 Athens, Greece
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Journal of Water Supply: Research and Technology-Aqua (2013) 62 (5): 296–308.
Article history
Received:
September 15 2012
Accepted:
April 12 2013
Citation
Eleni G. Farmaki, Nikolaos S. Thomaidis, Vasil Simeonov, Constantinos E. Efstathiou; Comparative use of artificial neural networks for the quality assessment of the water reservoirs of Athens. Journal of Water Supply: Research and Technology-Aqua 1 August 2013; 62 (5): 296–308. doi: https://doi.org/10.2166/aqua.2013.108
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