Neural network prediction for efficient waste management in Malaysia
Maintaining current municipal solid waste management (MSWM) for the next ten years would not be efficient anymore as it has brought many environmental issues such as air pollution. This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Gene...
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| Format: | Article |
| Language: | English English |
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Institute of Advanced Engineering and Science
2018
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| Online Access: | http://irep.iium.edu.my/66226/ http://irep.iium.edu.my/66226/1/66226_Neural%20network%20prediction%20for%20efficient.pdf http://irep.iium.edu.my/66226/2/66226_Neural%20network%20prediction%20for%20efficient_SCOPUS.pdf |
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| author | Yusoff, Siti Hajar Abdullah Din, Ummi Nur Kamilah Mansor, Hasmah Midi, Nur Shahida Zaini, Syasya Azra |
| author_facet | Yusoff, Siti Hajar Abdullah Din, Ummi Nur Kamilah Mansor, Hasmah Midi, Nur Shahida Zaini, Syasya Azra |
| author_sort | Yusoff, Siti Hajar |
| building | IIUM Repository |
| collection | Online Access |
| description | Maintaining current municipal solid waste management (MSWM) for the next ten years would not be efficient anymore as it has brought many environmental issues such as air pollution. This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on population growth factor. This study uses Malaysian population as sample size and the data for weight is acquired via authorized Malaysia statistics’ websites. All data will be normalized in the pre-processing stage before proceeding to the prediction using Visual Gene Developer. This project evaluated the performance using R2 value. Two hidden layers with ten and five nodes were used respectively. The result portrayed that there will be an increase of 29.03 percent of SWG in year 2031 compared to 2012. The limitation to this study is that the data was not based on real time as it was restricted by the government. |
| first_indexed | 2025-11-14T17:10:35Z |
| format | Article |
| id | iium-66226 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:10:35Z |
| publishDate | 2018 |
| publisher | Institute of Advanced Engineering and Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-662262018-09-12T02:01:54Z http://irep.iium.edu.my/66226/ Neural network prediction for efficient waste management in Malaysia Yusoff, Siti Hajar Abdullah Din, Ummi Nur Kamilah Mansor, Hasmah Midi, Nur Shahida Zaini, Syasya Azra TK4001 Applications of electric power Maintaining current municipal solid waste management (MSWM) for the next ten years would not be efficient anymore as it has brought many environmental issues such as air pollution. This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on population growth factor. This study uses Malaysian population as sample size and the data for weight is acquired via authorized Malaysia statistics’ websites. All data will be normalized in the pre-processing stage before proceeding to the prediction using Visual Gene Developer. This project evaluated the performance using R2 value. Two hidden layers with ten and five nodes were used respectively. The result portrayed that there will be an increase of 29.03 percent of SWG in year 2031 compared to 2012. The limitation to this study is that the data was not based on real time as it was restricted by the government. Institute of Advanced Engineering and Science 2018-11 Article PeerReviewed application/pdf en http://irep.iium.edu.my/66226/1/66226_Neural%20network%20prediction%20for%20efficient.pdf application/pdf en http://irep.iium.edu.my/66226/2/66226_Neural%20network%20prediction%20for%20efficient_SCOPUS.pdf Yusoff, Siti Hajar and Abdullah Din, Ummi Nur Kamilah and Mansor, Hasmah and Midi, Nur Shahida and Zaini, Syasya Azra (2018) Neural network prediction for efficient waste management in Malaysia. Indonesian Journal of Electrical Engineering and Computer Science, 12 (2). pp. 738-747. ISSN 2502-4752 http://www.iaescore.com/journals/index.php/IJEECS/article/view/14547/0 10.11591/ijeecs.v12.i2.pp738-747 |
| spellingShingle | TK4001 Applications of electric power Yusoff, Siti Hajar Abdullah Din, Ummi Nur Kamilah Mansor, Hasmah Midi, Nur Shahida Zaini, Syasya Azra Neural network prediction for efficient waste management in Malaysia |
| title | Neural network prediction for efficient waste management in Malaysia |
| title_full | Neural network prediction for efficient waste management in Malaysia |
| title_fullStr | Neural network prediction for efficient waste management in Malaysia |
| title_full_unstemmed | Neural network prediction for efficient waste management in Malaysia |
| title_short | Neural network prediction for efficient waste management in Malaysia |
| title_sort | neural network prediction for efficient waste management in malaysia |
| topic | TK4001 Applications of electric power |
| url | http://irep.iium.edu.my/66226/ http://irep.iium.edu.my/66226/ http://irep.iium.edu.my/66226/ http://irep.iium.edu.my/66226/1/66226_Neural%20network%20prediction%20for%20efficient.pdf http://irep.iium.edu.my/66226/2/66226_Neural%20network%20prediction%20for%20efficient_SCOPUS.pdf |