Improved Prediction of Hourly PM<sub>2.5</sub> Concentrations with a Long Short-Term Memory Optimized by Stacking Ensemble Learning and Ant Colony Optimization

Improved Prediction of Hourly PM<sub>2.5</sub> Concentrations with a Long Short-Term Memory Optimized by Stacking Ensemble Learning and Ant Colony Optimization

To address the performance degradation in existing PM<sub>2.5</sub> 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 op...

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Bibliographic Details
Main Authors: Zuhan Liu, Xianping Hong
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Toxics
Subjects:
PM<sub>2.5</sub>
long short-term memory network (LSTM)
ant colony optimization (ACO)
stacking ensemble learning
deep learning
Online Access:https://www.mdpi.com/2305-6304/13/5/327
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