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Forecasting Accuracy of Traditional Regression, Machine Learning, and Deep Learning: A Study of Environmental Emissions in Saudi Arabia

Author

Listed:
  • Suleman Sarwar

    (Department of Finance and Economics, College of Business, University of Jeddah, Jeddah 23445, Saudi Arabia)

  • Ghazala Aziz

    (Department of Business Administration, College of Administrative and Financial Sciences, Saudi Electronic University, Jeddah 13316, Saudi Arabia)

  • Daniel Balsalobre-Lorente

    (Department of Political Economy and Public Finance, Economics and Business Statistics and Economic Policy, University of Castilla-La Mancha, 13001 Ciudad Real, Spain)

Abstract

Currently, the world is facing the problem of climate change and other environmental issues due to higher emissions of greenhouse gases. Saudi Arabia is not an exception due to the dependence of the Saudi economy on fossil fuels, which adds to the problem. However, due to the nonlinear pattern of pollution-creating gases, including nitrogen and sulfur dioxide, it is not effortless to rely on forecasting accuracy. Nevertheless, it is essential to denoise the data to extract the reliable outcomes used by different econometric approaches. Hence, the current paper introduces a hybrid model combining compressed sensor denoising (CSD) with traditional regression, machine learning, and deep learning techniques. Comparing different hybrid models and various denoising techniques revealed that CSD-GAN is the best model for accurately predicting NO 2 and SO 2 , as compared with ARIMA, RLS, and SVR. Also, when the comparison is made between predicted and actual NO 2 and SO 2 levels, these are aligned, proving that CSD-GAN is superior in its level and direction of prediction. It can be concluded that the GAN model is the best hybrid model for predicting NO 2 and SO 2 emissions in Saudi Arabia. Hence, this model is recommended to policymakers for predicting environmental externalities and framing policies accordingly.

Suggested Citation

  • Suleman Sarwar & Ghazala Aziz & Daniel Balsalobre-Lorente, 2023. "Forecasting Accuracy of Traditional Regression, Machine Learning, and Deep Learning: A Study of Environmental Emissions in Saudi Arabia," Sustainability, MDPI, vol. 15(20), pages 1-22, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14957-:d:1261253
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    References listed on IDEAS

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    1. Yu, Lean & Zhao, Yang & Tang, Ling, 2014. "A compressed sensing based AI learning paradigm for crude oil price forecasting," Energy Economics, Elsevier, vol. 46(C), pages 236-245.
    2. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Yan-Fang Sang, 2013. "Improved Wavelet Modeling Framework for Hydrologic Time Series Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2807-2821, June.
    5. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    6. Kaijian He & Kin Keung Lai & Guocheng Xiang, 2012. "Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach," Energies, MDPI, vol. 5(4), pages 1-26, April.
    7. Marco Antonio Aceves-Fernández & Ricardo Domínguez-Guevara & Jesus Carlos Pedraza-Ortega & José Emilio Vargas-Soto, 2020. "Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-14, February.
    8. Struzik, Zbigniew R., 2001. "Wavelet methods in (financial) time-series processing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 296(1), pages 307-319.
    Full references (including those not matched with items on IDEAS)

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