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

Suleman Sarwar, Ghazala Aziz and Daniel Balsalobre-Lorente
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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

Sustainability, 2023, vol. 15, issue 20, 1-22

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.

Keywords: ARIMA; machine learning; deep learning; environment; forecasting; Saudi Arabia (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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