Journal Article

Improved Prediction of Hourly PM2.5 Concentrations with a Long Short-Term Memory Optimized by Stacking Ensemble Learning and Ant Colony Optimization

Hong, Xianping, Liu, Zuhan

2025

Overview

To address the performance degradation in existing PM2.5 prediction models caused by excessive complexity, poor spatiotemporal efficiency, and suboptimal parameter optimization, we employ stacking ensemble learning for feature weighting analysis and integrate the ant colony optimization (ACO) algorithm for model parameter optimization. Combining meteorological and collaborative pollutant data, a model (namely the stacking-ACO-LSTM model) with a much shorter consuming time than that of only long short-term memory (LSTM) networks suitable for PM2.5 concentration prediction is established. It can effectively filter out feature variables with higher weights, thereby reducing the predictive power of the model. The prediction of hourly PM2.5 concentration of the model is trained and tested using real-time monitoring data in Nanchang City from 2017 to 2019. The results show that the established stacking-ACO-LSTM model has high accuracy in predicting PM2.5 concentration, and compared to the same model without considering time and space efficiency and defective parameter optimization, the mean square error (MSE) decreases by about 99.88%, and the coefficient of determination (R2) increases by about 2.39%. This study provides a new idea for predicting PM2.5 concentration in cities.

• Publisher: MDPI AG,MDPI
• Subject: Air pollution / Algorithms / Ant colony optimization / ant colony optimization (ACO) / Artificial intelligence / Cities / deep learning / Ensemble learning / Learning / Long short-term memory / long short-term memory network (LSTM) / Neural networks / Outdoor air quality / Parameters / Particulate matter / Performance degradation / PM2.5 / Prediction models / Real time / stacking ensemble learning