Nature inspired Meta-heuristic optimization integrated with ensemble machine learning for PM2.5 modeling: a potential approach for sustainable eco-friendly health risk management

Particulate Matter 2.5 (PM 2.5) is a major air pollutant that can deeply penetrate the respiratory system and enter the bloodstream when inhaled. Therefore, it is significant to monitor and model PM 2.5, which is also considered as a key indicator of overall air quality. The current study employs the use of both Nature inspired Meta-heuristic optimization algorithms and Ensemble Machine learning (ML) techniques for the prediction of PM 2.5 using Sulfur dioxide (SO ₂ ), Nitrogen Dioxide (NO ₂ ), Respiratory suspended particulate matter (RSPM). Prior to dwelling into the modelling step, various pre-analysis techniques were conducted for data clean up and to understand the behaviour of the data. The quantitative performance results obtained from the Metaheuristic algorithms indicates that ANN-PSO outperformed all the other techniques including; SVR-BO, ENN-GA and LR. Furthermore, the quantitative outcomes indicate that ANN-PSO has the ability of improving the performance of the other techniques up to 80.4% and 73.2% in the calibration and validation phases respectively. More also, recent visualizations such as Fan plot and Bump chart were used in ranking the performance results obtained in PM 2.5 prediction. Moreover, Neural network ensemble (NNE) technique equally showed superior potentials over Simple average (SA) ensemble technique. To conclude, the quantitative and visualized performances of both the Metaheuristic algorithms and the ensemble paradigms indicates their importance in modelling and mitigation of PM 2.5 pollution, which requires concerted efforts at the local, and international levels to mitigate its effects and improve air quality on a global scale.