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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Bibliographic Details
Published in:	Modelling Vol. 6; no. 3; p. 69
Main Authors:	Huang, Tichen, Jiang, Yuyan, Gan, Rumeijiang, Wang, Fuyu
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 21.07.2025
Subjects:	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
Online Access:	Get full text
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