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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| Vydáno v: | Modelling Ročník 6; číslo 3; s. 69 |
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21.07.2025
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Gan, Rumeijiang Wang, Fuyu Huang, Tichen Jiang, Yuyan |
| Author_xml | – sequence: 1 givenname: Tichen surname: Huang fullname: Huang, Tichen – sequence: 2 givenname: Yuyan surname: Jiang fullname: Jiang, Yuyan – sequence: 3 givenname: Rumeijiang surname: Gan fullname: Gan, Rumeijiang – sequence: 4 givenname: Fuyu surname: Wang fullname: Wang, Fuyu |
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| SubjectTerms | 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 |
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