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A Novel One-Dimensional CNN with Exponential Adaptive Gradients for Air Pollution Index Prediction

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  • Mohammed G. Ragab

    (Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia)

  • Said J. Abdulkadir

    (Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia
    Centre for Research in Data Science (CERDAS), Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia)

  • Norshakirah Aziz

    (Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia
    Centre for Research in Data Science (CERDAS), Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia)

  • Qasem Al-Tashi

    (Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia
    Faculty of Administrative and Computer Sciences, University of Albaydha, Rada’a CV46+6X, Yemen)

  • Yousif Alyousifi

    (Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan, Bangi 43600, Malaysia)

  • Hitham Alhussian

    (Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia
    Centre for Research in Data Science (CERDAS), Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia)

  • Alawi Alqushaibi

    (Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia)

Abstract

Air pollution is one of the world’s most significant challenges. Predicting air pollution is critical for air quality research, as it affects public health. The Air Pollution Index (API) is a convenient tool to describe air quality. Air pollution predictions can provide accurate information on the future pollution situation, effectively controlling air pollution. Governments have expressed growing concern about air pollution due to its global effect on human health and sustainable growth. This paper proposes a novel forecasting model using One-Dimensional Deep Convolutional Neural Network (1D-CNN) and Exponential Adaptive Gradients (EAG) optimization to predict API for a selected location, Klang, a city in Malaysia. The proposed 1D-CNN–EAG exponentially accumulates past model gradients to adaptively tune the learning rate and converge in both convex and non-convex areas. We use hourly air pollution data over three years (January 2012 to December 2014) for training. Parameter optimization and model evaluation was accomplished by a grid-search with k-folds cross-validation. Results have confirmed that the proposed approach achieves better prediction accuracy than the benchmark models in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and the Correlation Coefficient (R-Squared) with values of 2.036, 2.354, 4.214 and 0.966, respectively, and time complexity.

Suggested Citation

  • Mohammed G. Ragab & Said J. Abdulkadir & Norshakirah Aziz & Qasem Al-Tashi & Yousif Alyousifi & Hitham Alhussian & Alawi Alqushaibi, 2020. "A Novel One-Dimensional CNN with Exponential Adaptive Gradients for Air Pollution Index Prediction," Sustainability, MDPI, vol. 12(23), pages 1-22, December.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:10090-:d:455496
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    References listed on IDEAS

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    1. Shay-Wei Choon & Hway-Boon Ong & Siow-Hooi Tan, 2019. "Does risk perception limit the climate change mitigation behaviors?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(4), pages 1891-1917, August.
    2. Hui Song & Jiejie Dai & Lingen Luo & Gehao Sheng & Xiuchen Jiang, 2018. "Power Transformer Operating State Prediction Method Based on an LSTM Network," Energies, MDPI, vol. 11(4), pages 1-15, April.
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    Cited by:

    1. Abdulrazak H. Almaliki & Abdessamed Derdour & Enas Ali, 2023. "Air Quality Index (AQI) Prediction in Holy Makkah Based on Machine Learning Methods," Sustainability, MDPI, vol. 15(17), pages 1-14, September.
    2. Süreyya Özöğür Akyüz & Pınar Karadayı Ataş & Aymane Benkhaldoun, 2024. "Predicting stock market by sentiment analysis and deep learning," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(2), pages 85-107.

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