Water quality index using fuzzy logic Utcubamba River, Peru
DOI:
https://doi.org/10.22267/rcia.203701.124Palabras clave:
Water analysis, water chemistry, water pollution, computer application, artificial intelligenceResumen
Water is a fundamental nutrient in the life of any living being. Therefore, it is necessary to estimate its quality, because it is an issue of increasing concern countries around the world for reasons such as the health of the population, regional, national and international economic development, and the environmental quality of the ecosystems. One tool that has been used to know the state of the water is the water quality indexes (WQI). The objective of this research was to develop a WQI based on fuzzy logic, which allows for the estimation of water quality in the Utcubamba River. The methodology used was proposed by Icaga in 2007. To evaluate the proposed WQI called "Diffuse Water Quality Index" (DWQI), sixteen points from the sampling conducted by the Research Institute for Sustainable Development during October 2014 on the Utcubamba River and its tributaries were used. To validate the index, it was necessary to estimate the correlation coefficient R2 between the results obtained and those of the NSF WQI wáter quality index reported by the Water Research Center. This new index presented results and reasonable correlation, R2 = 0.81. It is concluded that DWQI can be used as a tool for decision making in the water management of the Utcubamba River.
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APHA. (2005). Standard methods for the examination of water and wastewater. 21 st ed. Washington, D.C: APHA-AWWA-WEF.
Behar, R.; Zúñiga de Cardozo, M.; Rojas, O. (1997). Análisis y valoración del índice de calidad de agua (ICA) de la NSF: casos ríos Cali y Meléndez. Ingeniería y Competitividad. 1(1): 17 -27.
Castro, M.; Almeida, J.; Ferrer, J.; Díaz, D. (2014). Indicadores de la calidad del agua: evolución y tendencias a nivel global. Ingeniería Ambiental. 10(17): 111-124. doi: http://dx.doi.org/10.16925/in.v9i17.811
Cháves, J.; Leiva Tafur, D.; Corroto, F. (2016). Caracterización fisicoquímica y microbiológica de las aguas residuales en la ciudad de Chachapoyas, Región Amazonas. Ciencia Amazónica: (Iquitos), 6(1): 16-27.
Choupina, A.; Pereira, E. T.; Silva Soares, S.; Arruda, P.; Ribeiro, F. L.; Scalize, P. S. (2019). Water Quality Index: is it Possible to Measure with Fuzzy Logic? En: Kumar, K y Davim, J. P. Optimization for Engineering Problems. pp. 131-159. Primera edición. Estados Unidos: John Wiley & Sons, Inc. 182p.
DIGESA. (2007). Protocolo de Monitoreo de la Calidad Sanitaria de Recursos Hídricos Superficiales. http://www.digesa.minsa.gob.pe/depa/informes_tecnicos/PROTOCOLO-MONITOREO-CALIDAD-RECURSOS-HIDRICOS-SUPERFICIALES-(CONTINENTALES).pdf
Gamarra, O.; Corroto, F.; Barrena, M. A.; Rascón, J.; Chávez, J. (2018a). Calidad ecológica del agua en la cuenca del río Utcubamba, Amazonas, Perú. Primera edición. Perú: Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas. 157p.
Gamarra, O.; Barrena, M. A.; Barboza, E.; Rascón, J.; Corroto, F.; Taramona, L. A. (2018b). Fuentes de contaminación estacionales en la cuenca del río Utcubamba, región Amazonas, Perú. Arnaldoa. 25(1): 179-194.
García-Huamán, F. T.; Torres Delgado, J.; Vergara Medrano, S. E. (2011). Calidad ecológica del agua del río Utcubamba en relación a parámetros fisicoquímicos y biológicos. Amazonas-Perú. Revista de investigación científica Sciendo. 14(2): 7-19.
Gharibi, H.; Mahvi, A. H.; Nabizadeh, R.; Arabalibeik, H.; Yunesian, M.; Sowlat, M. H. (2012). A novel approach in water quality assessment based on fuzzy logic. Journal of Environmental Management. 112: 87-95. doi: https://doi.org/10.1016/j.jenvman.2012.07.007
Icaga, Y. (2007). Fuzzy evaluation of water quality classification. Ecological Indicators. 7(3): 710-718. doi: https://doi.org/10.1016/j.ecolind.2006.08.002
Lermontov, A.; Yokoyama, L.; Lermontov, M.; Machado, M. A. S. (2009). River quality analysis using fuzzy water quality index: Ribeira do Iguape River watershed, Brazil. Ecological Indicators. 9(6): 1188-1197.
Mishra, N.; Jha, P. (2014a). Fuzzy expert system and its utility in various fields. Recent Research in Science and Technology. 6(1): 41-45.
Mishra, N.; Jha, P. (2014b). Fuzzy expert system for drinking water quality index. Recent Research in Science and Technology. 6(1): 122-125.
Ocampo-Duque, W.; Ferre-Huguet, N.; Domingo, J. L.; Schuhmacher, M. (2006). Assessing water quality in rivers with fuzzy inference systems: A case study. Environment International. 32(6): 733-742.
Parastar, S.; Jalilzadeh, A.; Poureshg, Y.; Hashemi, M.; Rezaee, A.; Hossini, H. (2015). Assessment of national sanitation foundation water quality index and other quality characterization of Mamloo dam and supporting streams. Int J Env Health Eng. 4(1): 44.
Raman, B. V.; Bouwmeester, R.; Mohan, S. (2009). Fuzzy Logic Water Quality Index and the Importance of Water Quality Parameters. Air, Soil and Water Research. 2009(2): 51-59. doi: https://doi.org/10.4137/ASWR.S2156
Rodríguez, M. (2009). Lógica difusa como herramienta para interpretar datos de producción limpia en el sector agrícola. Idesia (Arica). 27(3): 101-105. doi: https://doi.org/10.4067/S0718-34292009000300012
Samboni, N. E.; Carvajal-Escobar, Y.; Escobar, J. C. (2007). Revisión de parámetros fisicoquímicos como indicadores de calidad y contaminación del agua. Ingeniería e Investigación. 27(3): 172-181.
Semiromi, B.; Hassani, A.; Torabian, A.; Karbassi, A. R.; Lotfi, H. (2011). Water quality index development using fuzzy logic: A case study of the Karoon River of Iran. African journal of biotechnology. 10: 10125-10133. doi: https://doi.org/10.5897/AJB11.1608
Serna, L. F. C. (2013). Un modelo de gestión de la calidad y cantidad de agua con lógica difusa gris para el río Aburrá. Revista de Ingenierías: Universidad de Medellín. 12(22): 59-74.
Srivastava, P.; Burande, A.; Sharma, N. (2013). Fuzzy Environmental Model for Evaluating Water Quality of Sangam Zone during Maha Kumbh 2013. Applied Computational Intelligence and Soft Computing. 2013: 265924. doi: https://doi.org/10.1155/2013/265924
Terrado, M.; Barceló, D.; Tauler, R.; Borrell, E.; de Campos, S. (2010). Surface-water-quality indices for the analysis of data generated by automated sampling networks. TrAC Trends in Analytical Chemistry. 29(1): 40-52.
Tiri, A.; Belkhiri, L.; Mouni, L. (2018). Evaluation of surface water quality for drinking purposes using fuzzy inference system. Groundwater for Sustainable Development. 6: 235-244. doi: https://doi.org/10.1016/j.gsd.2018.01.006
Torres, P.; Cruz, C. H.; 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.
Wayne, O. R. (1978). Environmental Indices: Theory and Practice. Primera edición. Estados Unidos. Ann Arbor, Mich.: Ann Arbor Science.
Water Research Center. (2018). Monitoring the Quality of Surface Waters (WQI Calculator). http://www.webcitation.org/73LKUC5s7
Zadeh, L.A. (1965). Fuzzy sets. Information and Control. 8(3): 338-353.
