Quantum-Enhanced Attention Neural Networks for PM2.5 Concentration Prediction

As industrialization and economic growth accelerate, PM2.5 pollution has become a critical environmental concern. Predicting PM2.5 concentration is challenging due to its nonlinear and complex temporal dynamics, limiting the accuracy and robustness of traditional machine learning models. To enhance...

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Vydané v:Modelling Ročník 6; číslo 3; s. 69
Hlavní autori: Huang, Tichen, Jiang, Yuyan, Gan, Rumeijiang, Wang, Fuyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 21.07.2025
Predmet:
Accuracy
Air pollution
Artificial intelligence
BiLSTM
BiTCN
Complexity
Economic development
Feature extraction
Global optimization
Machine learning
Neural networks
Outdoor air quality
PM2.5 concentration prediction
QCAM
QMEWOA
Robustness
Support vector machines
United States > US
China
Anhui China
Yangtze River
ISSN:2673-3951, 2673-3951
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Shrnutí:As industrialization and economic growth accelerate, PM2.5 pollution has become a critical environmental concern. Predicting PM2.5 concentration is challenging due to its nonlinear and complex temporal dynamics, limiting the accuracy and robustness of traditional machine learning models. To enhance prediction accuracy, this study focuses on Ma’anshan City, China and proposes a novel hybrid model (QMEWOA-QCAM-BiTCN-BiLSTM) based on an “optimization first, prediction later” approach. Feature selection using Pearson correlation and RFECV reduces model complexity, while the Whale Optimization Algorithm (WOA) optimizes model parameters. To address the local optima and premature convergence issues of WOA, we introduce a quantum-enhanced multi-strategy improved WOA (QMEWOA) for global optimization. A Quantum Causal Attention Mechanism (QCAM) is incorporated, leveraging Quantum State Mapping (QSM) for higher-order feature extraction. The experimental results show that our model achieves a MedAE of 1.997, MAE of 3.173, MAPE of 10.56%, and RMSE of 5.218, outperforming comparison models. Furthermore, generalization experiments confirm its superior performance across diverse datasets, demonstrating its robustness and effectiveness in PM2.5 concentration prediction.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2673-3951
2673-3951
DOI:10.3390/modelling6030069