Ensemble Machine Learning for Reliable Air Pollution Prediction and Sustainable Environmental Management

Air pollution poses significant threats to human health and environmental sustainability, requiring strong predictive models to monitor and forecast air quality. This research sought to develop and assess a resilient air pollution forecast model using data-driven modelling methodologies. The study used a thorough technique that included the compilation of worldwide air pollution datasets, succeeded by data pre-treatment and modification to guarantee the precision and pertinence of the input data. This data-centric methodology enabled the examination and interpretation of the dataset using several machine learning methods. The research examined the efficacy of several machine learning algorithms, including AdaBoost, Decision Tree, Extra Tree, Random Forest, Naïve Bayes, K-Nearest Neighbour (KNN), and Neural Network, in predicting diverse levels of air quality. Each algorithm was assessed according to precision, recall, F1-score, and overall accuracy, with specific emphasis on difficult air quality classifications. The findings indicated that some models, including Decision Tree, Extra Tree, Random Forest, and Neural Network, attained excellent accuracy and F1-scores, whilst others, such as AdaBoost and Naïve Bayes, exhibited deficiencies in managing certain air quality categories. An ensemble model was created to address these constraints and improve overall forecast accuracy by integrating the capabilities of the most effective algorithms. The ensemble model exhibited outstanding performance, attaining flawless precision, recall, F1-scores, and accuracy across all air quality categories, signifying its potential as a highly dependable instrument for real-time air quality monitoring and prediction. This research finds that the ensemble model signifies a substantial improvement in air pollution forecasting. Therefore, providing an effective option for environmental monitoring systems. The research underscores the significance of amalgamating several machine learning algorithms to enhance model resilience and precision, offering critical insights for public health administration and policy formulation.