Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia
It is crucial for developing countries such as Malaysia to be able to accurately predict future municipal solid waste generations in order to achieve high-quality waste management. The previous machine algorithm applied in the proposed study area Malaysia was an artificial neural network using NARX...
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Springer Science and Business Media B.V.
2025
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| author | Latif S.D. Hazrin N.A.B. Younes M.K. Ahmed A.N. Elshafie A. |
| author2 | 57216081524 |
| author_facet | 57216081524 Latif S.D. Hazrin N.A.B. Younes M.K. Ahmed A.N. Elshafie A. |
| author_sort | Latif S.D. |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | It is crucial for developing countries such as Malaysia to be able to accurately predict future municipal solid waste generations in order to achieve high-quality waste management. The previous machine algorithm applied in the proposed study area Malaysia was an artificial neural network using NARX inputs to accommodate the need of forecasting municipal solid waste generations in Malaysia. However, this approach is not highly accurate in today?s higher progressive state. Therefore, one of the aims of this research was to investigate the use of machine learning algorithms and its benefits. The machine learning algorithms investigated are specifically Gaussian process regression (GPR), ensemble of trees and neural networks. Each of these algorithms has its many strengths that could be altered according to the needs of users. For instance, various versions of neural networks are widely used for predicting municipal solid waste which includes the current approach adapted in the proposed study area. The findings indicated that the bagged tree model currently developed is not suitable for plotting a linear prediction although it managed to obtain a high performance of coefficient of determination (R2) = 0.92. Regarding GPR and neural network, the accuracy of the models was very high when every variable is included as a scenario which gives a perfect R2 = 1.00. The findings also showed that GPR and neural networks had the least error with root mean square error (RMSE) of 0.00009748 and 0.00099684, and mean absolute error (MAE) of 0.000071824 and 0.000672810, respectively. This study managed to fill in the gap of using GPR for predicting municipal solid waste generation. The outcome of this study could be of direct interest to public and private solid waste management companies in order to effectively manage solid waste through predicting the municipal solid waste generation accurately. ? The Author(s), under exclusive licence to Springer Nature B.V. 2023. |
| format | Article |
| id | my.uniten.dspace-36590 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Springer Science and Business Media B.V. |
| record_format | dspace |
| spelling | my.uniten.dspace-365902025-03-03T15:43:15Z Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia Latif S.D. Hazrin N.A.B. Younes M.K. Ahmed A.N. Elshafie A. 57216081524 58550394200 55966376900 57214837520 16068189400 Malaysia machine learning municipal solid waste numerical model prediction waste management It is crucial for developing countries such as Malaysia to be able to accurately predict future municipal solid waste generations in order to achieve high-quality waste management. The previous machine algorithm applied in the proposed study area Malaysia was an artificial neural network using NARX inputs to accommodate the need of forecasting municipal solid waste generations in Malaysia. However, this approach is not highly accurate in today?s higher progressive state. Therefore, one of the aims of this research was to investigate the use of machine learning algorithms and its benefits. The machine learning algorithms investigated are specifically Gaussian process regression (GPR), ensemble of trees and neural networks. Each of these algorithms has its many strengths that could be altered according to the needs of users. For instance, various versions of neural networks are widely used for predicting municipal solid waste which includes the current approach adapted in the proposed study area. The findings indicated that the bagged tree model currently developed is not suitable for plotting a linear prediction although it managed to obtain a high performance of coefficient of determination (R2) = 0.92. Regarding GPR and neural network, the accuracy of the models was very high when every variable is included as a scenario which gives a perfect R2 = 1.00. The findings also showed that GPR and neural networks had the least error with root mean square error (RMSE) of 0.00009748 and 0.00099684, and mean absolute error (MAE) of 0.000071824 and 0.000672810, respectively. This study managed to fill in the gap of using GPR for predicting municipal solid waste generation. The outcome of this study could be of direct interest to public and private solid waste management companies in order to effectively manage solid waste through predicting the municipal solid waste generation accurately. ? The Author(s), under exclusive licence to Springer Nature B.V. 2023. Final 2025-03-03T07:43:15Z 2025-03-03T07:43:15Z 2024 Article 10.1007/s10668-023-03882-x 2-s2.0-85173004744 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173004744&doi=10.1007%2fs10668-023-03882-x&partnerID=40&md5=170f574f3167bba7045765e1faf97cc9 https://irepository.uniten.edu.my/handle/123456789/36590 26 5 12489 12512 Springer Science and Business Media B.V. Scopus |
| spellingShingle | Malaysia machine learning municipal solid waste numerical model prediction waste management Latif S.D. Hazrin N.A.B. Younes M.K. Ahmed A.N. Elshafie A. Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia |
| title | Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia |
| title_full | Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia |
| title_fullStr | Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia |
| title_full_unstemmed | Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia |
| title_short | Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia |
| title_sort | evaluating different machine learning models for predicting municipal solid waste generation: a case study of malaysia |
| topic | Malaysia machine learning municipal solid waste numerical model prediction waste management |
| url_provider | http://dspace.uniten.edu.my/ |