An Ensemble Machine Learning Approach for Accurate Air Pollution Prediction and Environmental Monitoring - Olodiama, Ebikombo-ere; Afisi, Ogheneromesuo; Peter, Moses (International Journal of Computer Sciences and Engineering, 2025)
Air pollution presents substantial risks to public health and environmental sustainability, necessitating robust predictive models capable of monitoring and forecasting air quality. This study aimed to design and evaluate a robust air pollution prediction model by leveraging data-driven modeling techniques. The research employed a comprehensive methodology that involved the aggregation of global air pollution datasets, followed by data preprocessing and transformation to ensure the accuracy and relevance of the input data. This data-driven approach facilitated the analysis and interpretation of the dataset using various machine learning algorithms. The study explored the performance of several machine learning algorithms, including AdaBoost, Decision Tree, Extra Tree, Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN), and Neural Network, to determine their effectiveness in predicting different levels of air quality. Each algorithm was evaluated based on precision, recall, f1-score, and overall accuracy, with a particular focus on challenging air quality categories such as "Unhealthy" and "Very Unhealthy." The results revealed that while some models like Decision Tree, Extra Tree, Random Forest, and Neural Network achieved high accuracy and f1-scores, others such as AdaBoost and Naïve Bayes displayed limitations in handling certain air quality categories. To overcome these limitations and enhance the overall prediction accuracy, an ensemble model was developed by combining the strengths of the top-performing algorithms. The ensemble model demonstrated exceptional performance, achieving perfect precision, recall, f1-scores, and accuracy across all air quality categories, indicating its potential as a highly reliable tool for real-time air quality monitoring and prediction. This study concludes that the ensemble model represents a significant advancement in air pollution prediction. Hence, offering an efficient solution for environmental monitoring systems. The study highlights the importance of integrating multiple machine learning algorithms to improve model robustness and accuracy, providing valuable insights for public health management and policymaking. The study recommends further exploration of ensemble models in different geographic regions and the integration of real-time data from IoT devices to enhance the model's applicability and effectiveness in diverse environmental scenarios.