PM2.5 concentration prediction system combining fuzzy information granulation and multi-model ensemble learning

Abstract

With the rapid development of economy, air pollution caused by industrial expansion has caused serious harm to human health and social development. Therefore, establishing an effective air pollution concentration prediction system is of great scientific and practical significance for accurate and reliable predictions. This paper proposes a combination of point-interval prediction system for pollutant concentration prediction by leveraging neural network, meta-heuristic optimization algorithm, and fuzzy theory. Fuzzy information granulation technology is used in data preprocessing to transform numerical sequences into fuzzy particles for comprehensive feature extraction. The golden Jackal optimization algorithm is employed in the optimization stage to fine-tune model hyperparameters. In the prediction stage, an ensemble learning method combines training results from multiple models to obtain final point predictions while also utilizing quantile regression and kernel density estimation methods for interval predictions on the test set. Experimental results demonstrate that the combined model achieves a high goodness of fit coefficient of determination (R²) at 99.3 % and a maximum difference between prediction accuracy mean absolute percentage error (MAPE) and benchmark model at 12.6 %. This suggests that the integrated learning system proposed in this paper can provide more accurate deterministic predictions as well as reliable uncertainty analysis compared to traditional models, offering practical reference for air quality early warning.