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

Suleman Sarwar, Ghazala Aziz, Daniel Balsalobre-Lorente

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 NO2 and SO2, as compared with ARIMA, RLS, and SVR. Also, when the comparison is made between predicted and actual NO2 and SO2 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 NO2 and SO2 emissions in Saudi Arabia. Hence, this model is recommended to policymakers for predicting environmental externalities and framing policies accordingly.