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Assessment of the water quality monitoring network of the Piabanha River experimental watersheds in Rio de Janeiro, Brazil, using autoassociative neural networks

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Abstract

Water quality monitoring is a complex issue that requires support tools in order to provide information for water resource management. Budget constraints as well as an inadequate water quality network design call for the development of evaluation tools to provide efficient water quality monitoring. For this purpose, a nonlinear principal component analysis (NLPCA) based on an autoassociative neural network was performed to assess the redundancy of the parameters and monitoring locations of the water quality network in the Piabanha River watershed. Oftentimes, a small number of variables contain the most relevant information, while the others add little or no interpretation to the variability of water quality. Principal component analysis (PCA) is widely used for this purpose. However, conventional PCA is not able to capture the nonlinearities of water quality data, while neural networks can represent those nonlinear relationships. The results presented in this work demonstrate that NLPCA performs better than PCA in the reconstruction of the water quality data of Piabanha watershed, explaining most of data variance. From the results of NLPCA, the most relevant water quality parameter is fecal coliforms (FCs) and the least relevant is chemical oxygen demand (COD). Regarding the monitoring locations, the most relevant is Poço Tarzan (PT) and the least is Parque Petrópolis (PP).

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Acknowledgements

This work was funded by the Geological Survey of Brazil (CPRM), MCT/FINEP/CT-HIDRO, and Coordination of Higher Education Personnel Improvement-CAPES—Brazil (MEC/MCTI/CAPES/CNPq). The authors would like to thank CPRM EIBEX project team, especially Achiles Monteiro (in memoriam), for all the support and the anonymous reviewers whose comments brought to the author’s attention points that were unclear in the original draft.

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Authors and Affiliations

  1. CPRM—Geological Survey of Brazil, Rio de Janeiro, Brazil

    Mariana D. Villas-Boas

  2. Department of Civil Engineering, Texas A&M University, College Station, TX, USA

    Francisco Olivera

  3. Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Jose Paulo S. de Azevedo

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  1. Mariana D. Villas-Boas
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  2. Francisco Olivera
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  3. Jose Paulo S. de Azevedo
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Correspondence to Mariana D. Villas-Boas.

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Villas-Boas, M.D., Olivera, F. & de Azevedo, J.P.S. Assessment of the water quality monitoring network of the Piabanha River experimental watersheds in Rio de Janeiro, Brazil, using autoassociative neural networks. Environ Monit Assess 189, 439 (2017). https://doi.org/10.1007/s10661-017-6134-9

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