Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction
Air pollution, a significant global challenge driven by industrialization, urbanization, and population growth, is caused by the emission of harmful gases, particulates, and biological molecules into the atmosphere, posing serious risks to health and the environment. Key sources include power plant...
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IIUM Press, International Islamic University Malaysia
2025-01-01
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| Series: | International Islamic University Malaysia Engineering Journal |
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| Online Access: | https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/2818 |
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| author | Nor Irwin Basir Kathlyn Kaiyun Tan Danny Hartanto Djarum Zainal Ahmad Dai-Viet N. Vo Zhang Jie |
| author_facet | Nor Irwin Basir Kathlyn Kaiyun Tan Danny Hartanto Djarum Zainal Ahmad Dai-Viet N. Vo Zhang Jie |
| author_sort | Nor Irwin Basir |
| collection | DOAJ |
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Air pollution, a significant global challenge driven by industrialization, urbanization, and population growth, is caused by the emission of harmful gases, particulates, and biological molecules into the atmosphere, posing serious risks to health and the environment. Key sources include power plants, industrial activities, vehicles, and residential heating. Thus, effective air quality monitoring and forecasting are crucial to mitigating the adverse impacts of pollution. This paper presents shallow and deep sparse autoencoder artificial neural network models to improve the prediction of the Air Pollution Index (API) in Perak Darul Ridzuan, Malaysia, as a case study. The results show that the deep sparse autoencoder achieves better prediction accuracy with and values of 0.1474 and 0.8331, respectively, compared to 0.1515 and 0.8300 for the shallow sparse autoencoder. The performance of these autoencoder models is also compared with other models, such as feedforward artificial neural networks (FANN) and principal component analysis (PCA). The findings confirm that both autoencoder models enhance API prediction accuracy, with the deep sparse autoencoder emerging as the optimal model, highlighting the potential of deep learning in improving air quality prediction.
ABSTRAK: Pencemaran udara, merupakan satu cabaran global yang didorong oleh perindustrian, urbanisasi pesat, dan pertumbuhan populasi, adalah disebabkan oleh pelepasan gas, partikel, dan molekul biologi merbahaya ke atmosfera, menimbulkan risiko serius kepada kesihatan dan alam sekitar. Sumber utama termasuk loji janakuasa, aktiviti industri, kenderaan, dan pemanasan kediaman. Oleh itu pemantauan dan ramalan kualiti udara penting bagi mengurangkan kesan buruk pencemaran. Kajian ini membentangkan model rangkaian neural tiruan pengauto kod jarang ‘cetek’ dan pengauto kod jarang ‘dalam’ memperbaiki ramalan Indeks Pencemaran Udara (API) di negeri Perak Darul Ridzuan, Malaysia sebagai kes kajian. Dapatan kajian menunjukkan bahawa pengautokod jarang ‘dalam’ mencapai ketepatan ramalan lebih baik, dengan nilai MSE dan R2 masing-masing sebanyak 0.1474 dan 0.8331, berbanding 0.1515 dan 0.8300 bagi pengautokod jarang ‘cetek’. Prestasi model pengautokod ini juga dibandingkan dengan model lain, seperti rangkaian neural tiruan suapan hadapan (FANN) dan analisis komponen utama (PCA). Hasil kajian mengesahkan bahawa kedua-dua model pengautokod meningkatkan ketepatan ramalan API, dengan pengautokod jarang ‘dalam’ muncul sebagai model paling optimum, menonjolkan potensi pembelajaran mendalam ‘dalam’ meningkatkan ramalan kualiti udara.
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| format | Article |
| id | doaj-art-cf7a7313166044ac9bfa585353e7e928 |
| institution | OA Journals |
| issn | 1511-788X 2289-7860 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IIUM Press, International Islamic University Malaysia |
| record_format | Article |
| series | International Islamic University Malaysia Engineering Journal |
| spelling | doaj-art-cf7a7313166044ac9bfa585353e7e9282025-08-20T02:37:09ZengIIUM Press, International Islamic University MalaysiaInternational Islamic University Malaysia Engineering Journal1511-788X2289-78602025-01-0126110.31436/iiumej.v26i1.2818Autoencoder Artificial Neural Network Model for Air Pollution Index PredictionNor Irwin Basir0Kathlyn Kaiyun Tan1Danny Hartanto Djarum2Zainal Ahmad3https://orcid.org/0000-0003-4134-2173Dai-Viet N. Vo4Zhang Jie5https://orcid.org/0000-0002-9745-664XUniversiti Sains Malaysia Universiti Sains Malaysia Universiti Sains Malaysia Universiti Sains MalaysiaUniversiti Sains Malaysia Newcastle University Air pollution, a significant global challenge driven by industrialization, urbanization, and population growth, is caused by the emission of harmful gases, particulates, and biological molecules into the atmosphere, posing serious risks to health and the environment. Key sources include power plants, industrial activities, vehicles, and residential heating. Thus, effective air quality monitoring and forecasting are crucial to mitigating the adverse impacts of pollution. This paper presents shallow and deep sparse autoencoder artificial neural network models to improve the prediction of the Air Pollution Index (API) in Perak Darul Ridzuan, Malaysia, as a case study. The results show that the deep sparse autoencoder achieves better prediction accuracy with and values of 0.1474 and 0.8331, respectively, compared to 0.1515 and 0.8300 for the shallow sparse autoencoder. The performance of these autoencoder models is also compared with other models, such as feedforward artificial neural networks (FANN) and principal component analysis (PCA). The findings confirm that both autoencoder models enhance API prediction accuracy, with the deep sparse autoencoder emerging as the optimal model, highlighting the potential of deep learning in improving air quality prediction. ABSTRAK: Pencemaran udara, merupakan satu cabaran global yang didorong oleh perindustrian, urbanisasi pesat, dan pertumbuhan populasi, adalah disebabkan oleh pelepasan gas, partikel, dan molekul biologi merbahaya ke atmosfera, menimbulkan risiko serius kepada kesihatan dan alam sekitar. Sumber utama termasuk loji janakuasa, aktiviti industri, kenderaan, dan pemanasan kediaman. Oleh itu pemantauan dan ramalan kualiti udara penting bagi mengurangkan kesan buruk pencemaran. Kajian ini membentangkan model rangkaian neural tiruan pengauto kod jarang ‘cetek’ dan pengauto kod jarang ‘dalam’ memperbaiki ramalan Indeks Pencemaran Udara (API) di negeri Perak Darul Ridzuan, Malaysia sebagai kes kajian. Dapatan kajian menunjukkan bahawa pengautokod jarang ‘dalam’ mencapai ketepatan ramalan lebih baik, dengan nilai MSE dan R2 masing-masing sebanyak 0.1474 dan 0.8331, berbanding 0.1515 dan 0.8300 bagi pengautokod jarang ‘cetek’. Prestasi model pengautokod ini juga dibandingkan dengan model lain, seperti rangkaian neural tiruan suapan hadapan (FANN) dan analisis komponen utama (PCA). Hasil kajian mengesahkan bahawa kedua-dua model pengautokod meningkatkan ketepatan ramalan API, dengan pengautokod jarang ‘dalam’ muncul sebagai model paling optimum, menonjolkan potensi pembelajaran mendalam ‘dalam’ meningkatkan ramalan kualiti udara. https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/2818Air pollution index, Shallow sparse autoencoder, Deep sparse autoencoder, Prediction |
| spellingShingle | Nor Irwin Basir Kathlyn Kaiyun Tan Danny Hartanto Djarum Zainal Ahmad Dai-Viet N. Vo Zhang Jie Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction International Islamic University Malaysia Engineering Journal Air pollution index, Shallow sparse autoencoder, Deep sparse autoencoder, Prediction |
| title | Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction |
| title_full | Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction |
| title_fullStr | Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction |
| title_full_unstemmed | Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction |
| title_short | Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction |
| title_sort | autoencoder artificial neural network model for air pollution index prediction |
| topic | Air pollution index, Shallow sparse autoencoder, Deep sparse autoencoder, Prediction |
| url | https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/2818 |
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