Predicción de la generación de residuos sólidos domiciliarios con machine learning en una zona rural de Puno
DOI:
https://doi.org/10.21754/tecnia.v32i1.1378Palabras clave:
Desperdicios, Factor social, Algoritmos de machine learning, Gestión, Suburbios, DomicilioResumen
La gestión de residuos sólidos es uno de los principales desafíos ambientales en todas las ciudades del mundo debido a factores como el crecimiento poblacional y los hábitos de consumo. Una de las principales herramientas para el diseño de proyectos de gestión de residuos, es la estimación de la generación per cápita, sin embargo, el método tradicional para obtener esta información demanda mucho esfuerzo y tiempo, por ello esta investigación plantea un enfoque alternativo de la estimación de la generación per cápita a partir de factores socioeconómicos. Para ello se recogió información socio económica demográfica e información sobre la generación per cápita de residuos sólidos de 50 familias, posteriormente se determinaron las variables que tienen influencia significativa a partir del coeficiente de correlación ρ de Spearman para las variables numéricas y un ANOVA para las variables categóricas con un umbral de aceptación de 0.4 y 0.05 respectivamente. Las variables seleccionadas se utilizaron para entrenar los modelos de redes neuronales, regresión lineal múltiple, support vector machine, procesos gaussianos y random forest, cuyos desempeños fueron de R2 = 0.986, 0.982, 0.959, 0.837, 0.832; respectivamente. Para la validación se utilizó validación cruzada y partición de datos. Los resultados indican que las variables influyentes son el ingreso per cápita, el gasto en insumos y productos, el tamaño familiar y los servicios del hogar. Se concluye que las predicciones de los modelos son confiables (RMSE desde 8g a 27g) y a partir de ellas se pueden diseñar proyectos.
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[28] S. Keser, S. Duzgun, y A. Aksoy, “Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey”, Waste Manag., vol. 32, no. 3, pp. 359–371, 2012, doi: 10.1016/j.wasman.2011.10.017.
[29] S. O. Benítez, G. Lozano-Olvera, R. A. Morelos, y C. A. de Vega, “Mathematical modeling to predict residential solid waste generation”, Waste Manag., vol. 28, no. SUPPL. 1, pp. 7–13, 2008, doi: 10.1016/j.wasman.2008.03.020.
[30] S. Lebersorger y P. Beigl, “Municipal solid waste generation in municipalities: Quantifying impacts of household structure, commercial waste and domestic fuel”, Waste Manag., vol. 31, no. 9–10, pp. 1907–1915, 2011, doi: 10.1016/j.wasman.2011.05.016.
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[32] MINAM, “Guía para la caracterización de residuos sólidos municipales”, 2019. [En línea]. Disponible en: https://cdn.www.gob.pe/uploads/document/file/523785/Guía_para_la_caracterización_rsm-29012020__1_.pdf.
[33] T. V. Ramachandra, H. A. Bharath, G. Kulkarni, y S. S. Han, “Municipal solid waste: Generation, composition and GHG emissions in Bangalore, India”, Renew. Sustain. Energy Rev., vol. 82, no. September 2017, pp. 1122–1136, 2018, doi: 10.1016/j.rser.2017.09.085.
[34] I. Kononenko y M. Kukar, Machine learning and data mining: introduction to principles and algorithms, 2da ed., vol. 45, no. 07, West Sussex: Horwood Publishing Chichester, 2007.
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[2] DGGRS, “Valorización de residuos sóidos orgánicos municipales”, 2018.
[3] MINAM, “Portal Web SINIA: Residuos”, Sistema Nacional de Información Ambiental, 2021. https://sinia.minam.gob.pe/informacion/tematicas?tematica=08 (consultado ago. 04, 2021).
[4] U. Soni, A. Roy, A. Verma, y V. Jain, “Forecasting municipal solid waste generation using artificial intelligence models—a case study in India”, SN Appl. Sci., vol. 1, no. 2, pp. 1–10, 2019, doi: 10.1007/s42452-018-0157-x.
[5] A. P. Condori Iquise, “Factores socioeconómicos que inciden en la producción de residuos sólidos en el distrito de San Antonio de Esquilache, año 2015”, Universidad Nacional del Altiplano, 2017.
[6] J. K. Solano Meza, D. Orjuela Yepes, J. Rodrigo-Ilarri, y E. Cassiraga, “Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks”, Heliyon, vol. 5, no. 11, p. e02810, 2019, doi: 10.1016/j.heliyon.2019.e02810.
[7] N. E. Johnson et al., “Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City”, Waste Manag., vol. 62, pp. 3–11, 2017, doi: 10.1016/j.wasman.2017.01.037.
[8] A. Camero, J. Toutouh, J. Ferrer, y E. Alba, “Waste Generation Prediction in Smart Cities Through Deep Neuroevolution”, Commun. Comput. Inf. Sci., vol. 978, pp. 192–204, 2019, doi: 10.1007/978-3-030-12804-3_15.
[9] A. Kumar, S. R. Samadder, N. Kumar, y C. Singh, “Estimation of the generation rate of different types of plastic wastes and possible revenue recovery from informal recycling”, Waste Manag., vol. 79, pp. 781–790, 2018, doi: 10.1016/j.wasman.2018.08.045.
[10] S. Golbaz, R. Nabizadeh, y H. S. Sajadi, “Comparative study of predicting hospital solid waste generation using multiple linear regression and artificial intelligence”, J. Environ. Heal. Sci. Eng., vol. 17, no. 1, pp. 41–51, 2019, doi: 10.1007/s40201-018-00324-z.
[11] C. E. Kontokosta, B. Hong, N. E. Johnson, y D. Starobin, “Using machine learning and small area estimation to predict building-level municipal solid waste generation in cities”, Comput. Environ. Urban Syst., vol. 70, no. March, pp. 151–162, 2018, doi: 10.1016/j.compenvurbsys.2018.03.004.
[12] M. Abdallah, M. Abu Talib, S. Feroz, Q. Nasir, H. Abdalla, y B. Mahfood, “Artificial intelligence applications in solid waste management: A systematic research review”, Waste Manag., vol. 109, pp. 231–246, 2020, doi: 10.1016/j.wasman.2020.04.057.
[13] M. Kannangara, R. Dua, L. Ahmadi, y F. Bensebaa, “Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches”, Waste Manag., vol. 74, pp. 3–15, 2018, doi: 10.1016/j.wasman.2017.11.057.
[14] V. M. Adamović, D. Z. Antanasijević, M. Ristić, A. A. Perić-Grujić, y V. V. Pocajt, “An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level”, J. Mater. Cycles Waste Manag., vol. 20, no. 3, pp. 1736–1750, 2018, doi: 10.1007/s10163-018-0741-6.
[15] Z. Ceylan, “Estimation of municipal waste generation of Turkey using socio-economic indicators by Bayesian optimization tuned Gaussian process regression”, Waste Manag. Res., vol. 38, no. 8, pp. 840–850, 2020, doi: 10.1177/0734242X20906877.
[16] L. Chhay, M. A. H. Reyad, R. Suy, M. R. Islam, y M. M. Mian, “Municipal solid waste generation in China: influencing factor analysis and multi-model forecasting”, J. Mater. Cycles Waste Manag., vol. 20, no. 3, pp. 1761–1770, 2018, doi: 10.1007/s10163-018-0743-4.
[17] G. W. Cha, Y. C. Kim, H. J. Moon, y W. H. Hong, “New approach for forecasting demolition waste generation using chi-squared automatic interaction detection (CHAID) method”, J. Clean. Prod., vol. 168, pp. 375–385, 2017, doi: 10.1016/j.jclepro.2017.09.025.
[18] F. Wu, D. Niu, S. Dai, y B. Wu, “New insights into regional differences of the predictions of municipal solid waste generation rates using artificial neural networks”, Waste Manag., vol. 107, pp. 182–190, 2020, doi: 10.1016/j.wasman.2020.04.015.
[19] H. Niska y A. Serkkola, “Data analytics approach to create waste generation profiles for waste management and collection”, Waste Manag., vol. 77, pp. 477–485, 2018, doi: 10.1016/j.wasman.2018.04.033.
[20] R. Intharathirat, P. Abdul Salam, S. Kumar, y A. Untong, “Forecasting of municipal solid waste quantity in a developing country using multivariate grey models”, Waste Manag., vol. 39, pp. 3–14, may 2015, doi: 10.1016/J.WASMAN.2015.01.026.
[21] V. H. A. de M. Vieira y D. R. Matheus, “The impact of socioeconomic factors on municipal solid waste generation in São Paulo, Brazil”, Waste Manag. Res., vol. 36, no. 1, pp. 79–85, 2018, doi: 10.1177/0734242X17744039.
[22] T. V Ramachandra, H. A. Bharath, G. Kulkarni, y S. S. Han, “Municipal solid waste: Generation, composition and GHG emissions in Bangalore, India”, Renew. Sustain. Energy Rev., vol. 82, núm. September 2017, pp. 1122–1136, 2018, doi: 10.1016/j.rser.2017.09.085.
[23] K. A. Kolekar, T. Hazra, y S. N. Chakrabarty, “A Review on Prediction of Municipal Solid Waste Generation Models”, Procedia Environ. Sci., vol. 35, pp. 238–244, 2016, doi: 10.1016/j.proenv.2016.07.087.
[24] P. Beigl, S. Lebersorger, y S. Salhofer, “Modelling municipal solid waste generation: A review”, Waste Manag., vol. 28, no. 1, pp. 200–214, 2008, doi: 10.1016/j.wasman.2006.12.011.
[25] S. S. Chung, “Projecting municipal solid waste: The case of Hong Kong SAR”, Resour. Conserv. Recycl., vol. 54, no. 11, pp. 759–768, 2010, doi: 10.1016/j.resconrec.2009.11.012.
[26] C. Dai, Y. P. Li, y G. H. Huang, “A two-stage support-vector-regression optimization model for municipal solid waste management - A case study of Beijing, China”, J. Environ. Manage., vol. 92, no. 12, pp. 3023–3037, 2011, doi: 10.1016/j.jenvman.2011.06.038.
[27] N. P. Thanh, Y. Matsui, y T. Fujiwara, “Household solid waste generation and characteristic in a Mekong Delta city, Vietnam”, J. Environ. Manage., vol. 91, no. 11, pp. 2307–2321, 2010, doi: 10.1016/j.jenvman.2010.06.016.
[28] S. Keser, S. Duzgun, y A. Aksoy, “Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey”, Waste Manag., vol. 32, no. 3, pp. 359–371, 2012, doi: 10.1016/j.wasman.2011.10.017.
[29] S. O. Benítez, G. Lozano-Olvera, R. A. Morelos, y C. A. de Vega, “Mathematical modeling to predict residential solid waste generation”, Waste Manag., vol. 28, no. SUPPL. 1, pp. 7–13, 2008, doi: 10.1016/j.wasman.2008.03.020.
[30] S. Lebersorger y P. Beigl, “Municipal solid waste generation in municipalities: Quantifying impacts of household structure, commercial waste and domestic fuel”, Waste Manag., vol. 31, no. 9–10, pp. 1907–1915, 2011, doi: 10.1016/j.wasman.2011.05.016.
[31] INEI, “INEI pone a disposición del país dos sistemas de consulta sobre las características de la población y vivienda a nivel de manzana”, 2017. https://www.inei.gob.pe/prensa/noticias/inei-pone-a-disposicion-del-pais-dos-sistemas-de-consulta-sobre-las-caracteristicas-de-la-poblacion-y-vivienda-a-nivel-de-manzana-12162/ (consultado ago. 04, 2021).
[32] MINAM, “Guía para la caracterización de residuos sólidos municipales”, 2019. [En línea]. Disponible en: https://cdn.www.gob.pe/uploads/document/file/523785/Guía_para_la_caracterización_rsm-29012020__1_.pdf.
[33] T. V. Ramachandra, H. A. Bharath, G. Kulkarni, y S. S. Han, “Municipal solid waste: Generation, composition and GHG emissions in Bangalore, India”, Renew. Sustain. Energy Rev., vol. 82, no. September 2017, pp. 1122–1136, 2018, doi: 10.1016/j.rser.2017.09.085.
[34] I. Kononenko y M. Kukar, Machine learning and data mining: introduction to principles and algorithms, 2da ed., vol. 45, no. 07, West Sussex: Horwood Publishing Chichester, 2007.
[35] A. Kumar, S. R. Samadder, N. Kumar, y C. Singh, “Estimation of the generation rate of different types of plastic wastes and possible revenue recovery from informal recycling”, Waste Manag., vol. 79, pp. 781–790, 2018, doi: 10.1016/j.wasman.2018.08.045.
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Publicado
2022-06-30
Cómo citar
[1]
A. F. Cerna Cueva, C. W. Rosas Echevarría, R. S. Perales Flores, y P. L. Ataucusi Flores, «Predicción de la generación de residuos sólidos domiciliarios con machine learning en una zona rural de Puno», TEC, vol. 32, n.º 1, pp. 44–52, jun. 2022.
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