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Prediction of the quality of public water supply using artificial neural networks

2012; UWA Publishing; Volume: 61; Issue: 7 Linguagem: Inglês

10.2166/aqua.2012.014

1365-2087

Henrique Vicente, Susana Dias, Ana Fernandes, António Abelha, José Machado, José Neves,

Water Quality Monitoring Technologies

Research Article| November 01 2012 Prediction of the quality of public water supply using artificial neural networks Henrique Vicente; Henrique Vicente 1Escola de Ciências e Tecnologia, Departamento de Química e Centro de Química de Évora, Universidade de Évora, Rua Romão Ramalho, 59, 7000-671 Évora, Portugal E-mail: hvicente@uevora.pt Search for other works by this author on: This Site PubMed Google Scholar Susana Dias; Susana Dias 2Administração Regional de Saúde do Alentejo IP, Laboratório de Saúde Pública de Évora, Hospital do Patrocínio – 4° Piso, Av. Infante D. Henrique, 7000-811 Évora, Portugal Search for other works by this author on: This Site PubMed Google Scholar Ana Fernandes; Ana Fernandes 3Escola de Ciências e Tecnologia, Departamento de Química, Universidade de Évora, Rua Romão Ramalho, 59, 7000-671 Évora, Portugal Search for other works by this author on: This Site PubMed Google Scholar António Abelha; António Abelha 4Departamento de Informática, Universidade do Minho, Braga, Portugal Search for other works by this author on: This Site PubMed Google Scholar José Machado; José Machado 4Departamento de Informática, Universidade do Minho, Braga, Portugal Search for other works by this author on: This Site PubMed Google Scholar José Neves José Neves 4Departamento de Informática, Universidade do Minho, Braga, Portugal Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2012) 61 (7): 446–459. https://doi.org/10.2166/aqua.2012.014 Article history Received: February 17 2012 Accepted: September 07 2012 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThis Journal Search Advanced Search Citation Henrique Vicente, Susana Dias, Ana Fernandes, António Abelha, José Machado, José Neves; Prediction of the quality of public water supply using artificial neural networks. Journal of Water Supply: Research and Technology-Aqua 1 November 2012; 61 (7): 446–459. doi: https://doi.org/10.2166/aqua.2012.014 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex The Health Surveillance Program was established by the Regional Health Authority of Alentejo to control the quality of public water supply. This authority divides the water quality parameters into three distinct groups, namely P1 (pH and conductivity), P2 (nitrate and manganese) and P3 (sodium and potassium), for which the sampling frequency is dissimilar. Thus, the development of formal models is essential to predict the chemical parameters included in group P2 and included in group P3, for which the sampling frequency is lower, based on the chemical parameters included in group P1. In the present work, artificial neural networks (ANNs) were used to predict the concentration of nitrate, manganese, sodium and potassium from pH and conductivity. Different network structures have been elaborated and evaluated using the mean absolute deviation and the mean squared error. The ANN selected to predict the concentration of nitrate, sodium and potassium from pH and conductivity has a 2-18-14-3 topology while the network selected to predict the concentration of nitrate and manganese has a 2-19-10-2 topology. A good match between the observed and predicted values was observed with the R2 values varying in the range 0.9960–0.9989 for the training set and 0.9993–0.9952 for the test set. artificial neural networks, monitoring of public water supply, prediction of water quality parameters This content is only available as a PDF. © IWA Publishing 2012 You do not currently have access to this content.