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

Forecast generation model of municipal solid waste using multiple linear regression


  • 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.


Main Subjects

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