Forecast generation model of municipal solid waste using multiple linear regression
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 da...
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GJESM Publisher
2020-01-01
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| Series: | Global Journal of Environmental Science and Management |
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| Online Access: | https://www.gjesm.net/article_36862_6d63e6591153a2e020ee980970ad5843.pdf |
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| author | J.A. Araiza-Aguilar M.N. Rojas-Valencia R.A. Aguilar-Vera |
| author_facet | J.A. Araiza-Aguilar M.N. Rojas-Valencia R.A. Aguilar-Vera |
| author_sort | J.A. Araiza-Aguilar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3994d606e60b4d659da51c06dbb34f93 |
| institution | Kabale University |
| issn | 2383-3572 2383-3866 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | GJESM Publisher |
| record_format | Article |
| series | Global Journal of Environmental Science and Management |
| spelling | doaj-art-3994d606e60b4d659da51c06dbb34f932025-02-02T15:16:21ZengGJESM PublisherGlobal Journal of Environmental Science and Management2383-35722383-38662020-01-016111410.22034/GJESM.2020.01.0136862Forecast generation model of municipal solid waste using multiple linear regressionJ.A. Araiza-Aguilar0M.N. Rojas-Valencia1R.A. Aguilar-Vera2School of Environmental Engineering, University of Science and Arts of Chiapas, North beltway, Lajas Maciel, Tuxtla Gutierrez, Chiapas, MexicoInstitute of Engineering, National Autonomous University of Mexico, External circuit, University City, Coyoacan delegation, Mexico City, MexicoInstitute of Geography, National Autonomous University of Mexico, External circuit, University City, Coyoacan delegation, Mexico City, MexicoThe 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.https://www.gjesm.net/article_36862_6d63e6591153a2e020ee980970ad5843.pdfexplanatory variablesforecast modelmultiple linear regressionstatistical analysiswaste generation |
| spellingShingle | J.A. Araiza-Aguilar M.N. Rojas-Valencia R.A. Aguilar-Vera Forecast generation model of municipal solid waste using multiple linear regression Global Journal of Environmental Science and Management explanatory variables forecast model multiple linear regression statistical analysis waste generation |
| title | Forecast generation model of municipal solid waste using multiple linear regression |
| title_full | Forecast generation model of municipal solid waste using multiple linear regression |
| title_fullStr | Forecast generation model of municipal solid waste using multiple linear regression |
| title_full_unstemmed | Forecast generation model of municipal solid waste using multiple linear regression |
| title_short | Forecast generation model of municipal solid waste using multiple linear regression |
| title_sort | forecast generation model of municipal solid waste using multiple linear regression |
| topic | explanatory variables forecast model multiple linear regression statistical analysis waste generation |
| url | https://www.gjesm.net/article_36862_6d63e6591153a2e020ee980970ad5843.pdf |
| work_keys_str_mv | AT jaaraizaaguilar forecastgenerationmodelofmunicipalsolidwasteusingmultiplelinearregression AT mnrojasvalencia forecastgenerationmodelofmunicipalsolidwasteusingmultiplelinearregression AT raaguilarvera forecastgenerationmodelofmunicipalsolidwasteusingmultiplelinearregression |