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
Copyright © 2015 Elsevier Ltd. All rights reserved.
| Veröffentlicht in: | Waste management (New York, N.Y.). - 1999. - 48(2016) vom: 19. Feb., Seite 14-23 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2016
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| Zugriff auf das übergeordnete Werk: | Waste management (New York, N.Y.) |
| Schlagworte: | Journal Article Artificial neural network Fars Province Multiple linear regression Seasonal municipal solid waste generation Solid Waste |
| Zusammenfassung: | Copyright © 2015 Elsevier Ltd. All rights reserved. Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate |
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| Beschreibung: | Date Completed 06.10.2016 Date Revised 10.12.2019 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1879-2456 |
| DOI: | 10.1016/j.wasman.2015.09.034 |