Air Quality Forecasting Based on Socio-Economic and Environmental Indicators: Combining Statistical and Machine Learning Techniques

This research aims to model and predict greenhouse gas (GHG) emissions in Saudi Arabia by examining their association with crucial socio-economic and environmental factors. Utilizing annual data from 1980 to 2023, the study focuses on three emission variables as dependent variables: carbon dioxide (CO₂) emissions from the power sector, methane (CH₄) emissions from the power sector, and nitrous oxide (N₂O) emissions from industrial activities. The independent variables include agricultural land area, urban population, GDP growth, exports, trade openness, foreign direct investment, and manufacturing output. A comparative assessment of various modeling approaches Ordinary Least Squares (OLS), Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (Enet), Random Forest (RF), and a new hybrid method that merges Elastic Net and Random Forest (ENRF) was performed. The performance of the models was evaluated based on Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The results indicated that the ENRF model consistently surpassed both traditional and machine learning techniques, achieving the lowest MSE and RMSE values. The outcomes underscore the efficacy of hybrid statistical and machine learning models in reliably predicting emissions and informing environmental policy in complex, big data contexts.