1 School of Environmental Engineering, University of Science and Arts of Chiapas, North beltway, Lajas Maciel, Tuxtla Gutierrez, Chiapas, Mexico

2 Institute of Engineering, National Autonomous University of Mexico, External circuit, University City, Coyoacan delegation, Mexico City, Mexico

3 Institute of Geography, National Autonomous University of Mexico, External circuit, University City, Coyoacan delegation, Mexico City, Mexico


The objective of this study was to develop a forecast model to determine the rate of generation of municipal solid waste in the municipalities of the Cuenca del Cañón del Sumidero, Chiapas, Mexico. Multiple linear regression was used with social and demographic explanatory variables. The compiled database consisted of 9 variables with 118 specific data per variable, which were analyzed using a multicollinearity test to select the most important ones. Initially, different regression models were generated, but only 2 of them were considered useful, because they used few predictors that were statistically significant. The most important variables to predict the rate of waste generation in the study area were the population of each municipality, the migration and the population density. Although other variables, such as daily per capita income and average schooling are very important, they do not seem to have an effect on the response variable in this study. The model with the highest parsimony resulted in an adjusted coefficient of 0.975, an average absolute percentage error of 7.70, an average absolute deviation of 0.16 and an average root square error of 0.19, showing a high influence on the phenomenon studied and a good predictive capacity.

Graphical Abstract


  • The predictive model developed was constructed using multiple linear regression, with explanatory social and demographic variables that met the multicollinearity test;
  • The results of this work show that the variables most influential on waste generation are directly related to the number of inhabitants; such as population, population density and population born in another entity;
  • The suggested predictive model has a high parsimony, in addition, the adjusted coefficient of determination and the accuracy coefficients indicated high influence on the explained phenomenon and a high forecasting capacity.


Abdoli, M.; Falahnezhad, M.; Behboudian, S., (2011). Multivariate econometric approach for solid waste generation modeling: a case study of Mashhad, Iran. Environ. Eng. Sci., 28(9): 627-633 (7 pages).

Agirre, E.; Ibarra, G.; Madariaga, I., (2006). Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area. Environ. Modell. Software. 21(4): 430–446 (17 pages).

Alvarado, H.; Nájera, H.; González, F.; Palacios, R., (2009). Study of generation and characterization of household solid waste in the municipal seat of Chiapa de Corzo, Chiapas, Mexico. Lacandonia J., 3: 85-92 (8 pages).

Araiza, J.; López, C.; Ramírez, N., (2015). Municipal solid waste management: case study in Las Margaritas, Chiapas. AIDIS J. Eng. Environ. Sci.: Res. Develop. Pract., 8(3): 299-311 (13 pages).

Arriaza, M., (2006). Practical guide to data analysis, junta de Andalucía, ministry of innovation, science and business, institute of agricultural research and training and fishing, Spain.

Azadi, S.; Karimí, A., (2016). Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: a case study of Fars province, Iran. Waste Manage., 48: 14-23 (10 pages).

Beigl, P.; Lebersorger, S.; Salhofer, S., (2008). Modelling municipal solid waste generation: a review. Waste Manage., 28(1): 200-214 (15 pages).

Bel, G.; Mur, M., (2009). Intermunicipal cooperation, privatization and waste management costs: evidence from rural municipalities. Waste Manage., 29(10): 2772–2778 (7 pages).

Buenrostro, O.; Bocco, G.; Vence, J., (2001). Forecasting generation of urban solid waste in developing countries - a case study in Mexico. J. Air Waste Manage., 51(1): 86-93 (8 pages).

Chang, Y.; Lin, C.; Chyan, J.; Chen, I.; Chang, J. (2007). Multiple regression models for the lower heating value of municipal solid waste in Taiwan. J. Environ. Manage., 85(4): 891–899 (9 pages).

Chhay, L.; Reyad, M.; Suy, R.; Islam, M.; Mian M., (2018). Municipal solid waste generation in China: influencing factor analysis and multi-model forecasting. J. Mater. Cycles Waste Manage., 20(3): 1761–1770 (10 pages).

CONAPO, (2017). National Population Council. Municipal Marginalization Index.

Ghinea, C.; Niculina, E.; Comanita, E.; Gavrilescu, M.; Campean, T.; Curteanu, S.; Gavrilescu, M. (2016). Forecasting municipal solid waste generation using prognostic tools and regression analysis. J. Environ. Manage., 182: 80-93 (14 pages).

Grazhdani, D., (2016). Assessing the variables affecting on the rate of solid waste generation and recycling: an empirical analysis in Prespa Park. Waste Manage., 48: 3-13 (11 pages).

INEGI, (2010). Population and housing census 2010: interactive data query. National Institute of Statistic and Geography.

Intharathirat, R.; Salam, P.; Kumar, S.; Untong, A., (2015). Forecasting of municipal solid waste quantity in a developing country using multivariate grey model. Waste Manage., 39: 3–14 (12 pages).

Kannangara, M.; Dua, R.; Ahmadi, L.; Bensebaa, F., (2018). Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. Waste Manage., 74: 3–15 (13 pages).

Khan, D.; Kumar, A.; Samadder, S., (2016). Impact of socioeconomic status on municipal solid waste generation rate. Waste Manage., 49: 15-25 (11 pages).

Keser, S.; Duzgun, S.; Aksoy, A., (2012). Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey. Waste Manage., 32(3): 359–371 (13 pages).

Kolekar, K.; Hazra, T.; Chakrabarty, S., (2016). A review on prediction of municipal solid waste generation models. Procedia Environ Sci., 35: 238 – 244 (7 pages).

Kumar, A.; Samandder, S., (2017). An empirical model for prediction of household solid waste generation rate – a case study of Dhanbad, India. Waste Manage., 68: 3-15 (13 pages).

Liu, C.; Wu, X., (2010). Factors influencing municipal solid waste generation in China: a multiple statistical analysis study. Waste Manage. Res., 29(4): 371-378 (8 pages).

Liu, J.; Li, Q.; Gu, W.; Wang, C., (2019). The Impact of consumption patterns on the generation of municipal solid waste in China: evidences from provincial data. Int. J. Environ. Res. Public Health, 16(10): 1-19 (19 pages).

Mahmood, S.; Sharif, F.; Rahman, A.U.; Khan, A.U., (2018). Analysis and forecasting of municipal solid waste in Nankana City using geo-spatial techniques. Environ. Monit. Assess., 190(5): 1-14 (14 pages).

Márquez, M.; Ojeda, S.; Hidalgo, H., (2008). Identification of behavior patterns in household solid waste generation in Mexicali’s City: study case. Resour. Conserv. Recycl., 52(11): 1299–1306 (8 pages).

Mendenhall, W.; Sincich, T., (2012). A second course in statistics regression analysis, Prentice Hall, United States of America.

OECD, (2015). Measuring well-being in Mexican States. Organization for Economic Cooperation and Development. OECD Publishing, Paris, France.

Ojeda, S.; Lozano, G.; Morelos, R.; Armijo, C., (2008). Mathematical modeling to predict residential solid waste generation. Waste Manage., 28(1): S7–S13 (7 pages).

Pan, A.; Yu, L.; Yang, Q, (2019). Characteristics and forecasting of municipal solid waste generation in China. Sustainability, 11(5): 1-11 (11 pages).

Pires, J.; Martins, F.; Sousa, S.; Alvim, M.; Pereira, M. (2008). Selection and validation of parameters in multiple linear and principal component regressions. Environ. Modell. Softw., 23(1): 50-55 (6 pages).

Rodríguez, M. (2004). Design of a mathematical model of municipal solid waste generation in Nicolás Romero, Mexico. Master's Thesis, National Polytechnic Institute, Mexico.

Rybová, K.; Slavik, J.; Burcin, B.; Soukopová, J.; Kučera, T.; Černíková, A., (2018). Socio-demographic determinants of municipal waste generation: case study of the Czech Republic. J. Mater. Cycles Waste Manage., 20(3): 1884–1891 (8 pages).

Shan, C., (2010). Projecting municipal solid waste: the case of Hong Kong SAR. Resour. Conserv. Recycl., 54(11): 759–768 (10 pages).

Soni, U.; Roy, A.; Verma, A.; Jain, V., (2019). Forecasting municipal solid waste generation using artificial intelligence models—a case study in India. SN Appl. Sci., 1(2): 1-11 (11 pages).



Araiza-Aguilar, J.A.; Rojas-Valencia, M.N.;  Aguilar-Vera, R.A., (2020). Forecast generation model of municipal solid waste using multiple linear regression. Global J. Environ. Sci. Manage., 6(1): …,

Letters to Editor

GJESM Journal welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in GJESM should be sent to the editorial office of GJESM within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.
[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.
[3] Letters can be no more than 300 words in length.
[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.
[5] Anonymous letters will not be considered.
[6] Letter writers must include their city and state of residence or work.
[7] Letters will be edited for clarity and length.