Artificial neural network to estimate an index of water quality
DOI:
https://doi.org/10.29019/enfoque.v11n2.633Keywords:
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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References
Alizadeh, M. J., y Reza Kavianpour, M. (2015). Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. Marine pollution bulletin, 98 (1-2): 171-178.
Behar, R., Zúñiga de Cardozo, Ma. C. y Rojas, O. (1997). Análisis y valoración del índice de calidad de agua (ICA) de la NSF: casos ríos Cali y Meléndez. Revista Ingeniería y Copetitividad, 1 (1): 17-27.
Chau, K. (2006). A review on integration of artificial intelligence into water quality modelling. Marine Pollution Bulletin, 52 (7): 726-733.
Dawson, C. W., y R. L. Wilby. (2001). Hydrological Modelling Using Artificial Neural Networks. Progress in Physical Geography: Earth and Environment, 25 (1): 80-108.
Diamantopoulou, M. J., Papamichail, D. M. y Antonopoulos, V. Z. (2005). The Use of a Neural Network Technique for the Prediction of Water Quality Parameters. Operational Research, 5 (1): 115-125.
Dogan, E., Sengorur, B. y Koklu, R. (2009). Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. Journal of Environmental Management, 90 (2): 1229-1235.
Federico Bertona, L. (2005). Entrenamiento de redes neuronales basado en algoritmos evolutivos [Tesis de grado en Ingeniería Informática, Universidad de Buenos Aires]. http://laboratorios.fi.uba.ar/lsi/bertona-tesisingenieriainformatica.pdf
Gamarra, O., Corroto, F., Rascón, J. y Chávez, J. (2018). Calidad ecológica del agua en la cuenca del río Utcubamba, Amazonas, Perú (Primera). Primera. Chachapoyas: Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas.
Gazzaz, N. M., Yusoff, M. K., Zaharin Aris, A., Juahir, H. y Firuz Ramli. M. (2012). Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Marine Pollution Bulletin, 64 (11): 2409-2420.
Gómez Rojas, G. A., Henao López, J. C. y Salazar Isaza, H. (2004). Entrenamiento de una red neuronal artificial usando el algoritmo simulated annealing. Scientia Et Technica, X: 13-18.
Flórez López, R., Lévy Mangin, J. P. y Fernández Fernández, J. M. (2008). Las redes neuronales artificiales. Netbiblo: Tirant lo Blanch.
Maier, H. R., y Graeme C. Dandy. (1996). The use of artificial neural networks for the prediction of water quality parameters. Water Resources Research 32 (4): 1013-1022.
May, D., y Muttucumaru S. (2009). Prediction of urban stormwater quality using artificial neural networks. Faculty of Engineering - Papers (Archive): 296-302.
Najah, A., Elshafie, A., Karim, O. A. y Jaffar, O. (2009). Prediction of Johor River water quality parameters using artificial neural networks. European Journal of Scientific Research, 28 (3): 422-435.
Palani, S., Liong, S.-Y., y Tkalich, P. (2008). An ANN Application for Water Quality Forecasting. Marine Pollution Bulletin, 56 (9): 1586-1597.
Sancho Caparrini, F. (2017). Entrenamiento de Redes Neuronales: Mejorando el Gradiente Descendiente. http://www.cs.us.es/~fsancho/?e=165.
Singh, K. P., Basant, A., Malik, A. y Jain, G. (2009). Artificial neural network modeling of the river water quality—a case study. Ecological Modelling, 220 (6):8 88-895.
Solaimany-Aminabad, M, Maleki, A. y Hadi, M. (2013). Application of artificial neural network (ANN) for the prediction of water treatment plant influent characteristics. Journal of advances in environmental health research, 1 (2): 89-100.
Srivastava, P., Burande, A. y Sharma, N. (2013). Fuzzy Environmental Model for Evaluating Water Quality of Sangam Zone during Maha Kumbh 2013. Applied Computational Intelligence and Soft Computing. Recuperado 24 de septiembre de 2018 https://www.hindawi.com/journals/acisc/2013/265924/.
Thambavani D., S., y Uma, M. (2014). Numerical Study of Back Propagation Learning Algorithms for Forecasting Water Quality Index. IJERST, 3 (3): 1548-1555.
Torres, P., Hernán Cruz, C. y Patiño, P. J. (2009). Índices de calidad de agua en fuentes superficiales utilizadas en la producción de agua para consumo humano. Una revisión crítica. Revista Ingenierías Universidad de Medellín, 8 (15 Sup. 1):79-94.
Water Research Center. (2018). Water Quality Index Calculator. Recuperado 9 de noviembre de 2018 (https://www.water-research.net/index.php/water-treatment/water-monitoring/monitoring-the-quality-of-surfacewaters).
Wu, Wenyan, G., Dandy, C. y Maier, H. R. (2014). Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling. Environmental Modelling & Software, 54: 108-127.
Zare Abyaneh, H. (2014). Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters. Journal of Environmental Health Science and Engineering 12 (1): 40.
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