Prediction of solid household waste generation with machine learning in a rural area of Puno
DOI:
https://doi.org/10.21754/tecnia.v32i1.1378Keywords:
Waste, Social factor, Machine learning algorithms, Management, Suburbs, DomicileAbstract
Solid waste management is one of the main environmental challenges in cities around the world due to factors such as population growth and consumption habits. One of the main tools for the design of waste management projects is the estimation of per capita generation, however, the traditional method to obtain this information demands a lot of effort and time, therefore this research proposes an alternative approach to estimate per capita generation based on socioeconomic factors. For this purpose, socioeconomic demographic information and information on the per capita generation of solid waste of 50 families was collected, subsequently the variables that have significant influence were determined from the correlation coefficient ρ of Spearman for numerical variables and an ANOVA for categorical variables with an acceptance threshold of 0.4 and 0.05 respectively. The selected variables were used to train the neural network, multiple linear regression, support vector machine, Gaussian process and random forest models, whose performances were R2 = 0.986, 0.982, 0.959, 0.837, 0.832; respectively. Cross validation and data partitioning were used for validation. The results indicate that the influential variables are per capita income, expenditure on supplies and products, family size and household services. It is concluded that the predictions of the models are reliable (RMSE from 8g to 27g) and from them projects can be designed.
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References
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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.
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[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.
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[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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Published
2022-06-30
How to Cite
[1]
A. F. Cerna Cueva, C. W. Rosas Echevarría, R. S. Perales Flores, and P. L. Ataucusi Flores, “Prediction of solid household waste generation with machine learning in a rural area of Puno”, TEC, vol. 32, no. 1, pp. 44–52, Jun. 2022.
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Environmental engineering
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