Artificial neural network to estimate an index of water quality

Authors

  • Lenin Quiñones Huatangari Universidad Nacional de Jaén
  • Luis Ochoa Toledo Universidad Nacional Autónoma de México
  • Nicolás Kemper Valverde Universidad Nacional Autónoma de México
  • Oscar Gamarra Torres Universidad Nacional Toribio Rodríguez de Mendoza
  • José Bazán Correa Universidad Nacional de Piura
  • Jorge Delgado Soto Universidad Nacional de Jaén

DOI:

https://doi.org/10.29019/enfoque.v11n2.633

Keywords:

Water quality index; artificial neural networks; multilayer perceptron; physical-chemical parameters.

Abstract

The artificial neural network (RNA) is a computational model that emulates the biological neural system in information processing. The originating models are suitable for the purpose of describing long-term specifics, in addition to nonlinear relationships. This tool is used to predict physical chemical and microbiological parameters that influence water quality. The United States National Sanitation Foundation proposed a water quality index, known as the NSF WQI. This article describes the design, training and use of the three-layer neural perceptron neural model for the calculation of the NSF WQI of the Utcubamba River and its tributaries. Using the Matlab software and applying the Levenberg-Marquardt training algorithm, the optimal RNA architecture was found to be 6-12-1, plus the percentage for the training, validation, and test sets of 70 %, 10 %, and 20 % respectively. RNA performance has been evaluated using the root of the root mean square error (RMSE) and the correlation coefficient (R). High correlations (greater than 0.94) were made between the measured and predicted values. Finally, the RNA proposal offers a useful alternative for the calculation and prediction of the water quality index in relation to dissolved oxygen (DO), biochemical demand for oxygen (BOD), nitrates, fecal coliforms, potential for hydrogen ions (pH) and turbidity.

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Published

2020-04-01

How to Cite

Quiñones Huatangari, L., Ochoa Toledo, L., Kemper Valverde, N., Gamarra Torres, O., Bazán Correa, J., & Delgado Soto, J. (2020). Artificial neural network to estimate an index of water quality. Enfoque UTE, 11(2), pp. 109-120. https://doi.org/10.29019/enfoque.v11n2.633

Issue

Section

Miscellaneous