期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 12, 页码 8514-8522出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3065425
关键词
Forecasting; Neural networks; Predictive models; Long short term memory; Deep learning; Time series analysis; Biological system modeling; Discrete stationary wavelet transform (DSWT); multichannel neural network; multitask neural network; nested long short term memory (NLSTM)
Artificial intelligence-based air quality index (AQI) forecasting is a hot research topic, and the proposed multiple nested long short term memory network (MTMC-NLSTM) model performs superior in accurate AQI forecasting.
Artificial intelligence-based air quality index (AQI) forecasting is a hot research topic in the fields of sustainable and smart industrial environment design. There are mainly two obstacles that hinder the existing machine learning (ML) and deep learning (DL) technologies providing accurate forecasting results to protect the environment, which include the intercorrelation between different AQI components and the highly volatile AQI pattern changes. In this article, a novel DL framework combining multiple nested long short term memory networks (MTMC-NLSTM) is proposed for accurate AQI forecasting enlightened with the federated learning. The performance of the proposed MTMC-NLSTM model is compared with conventional ML models, DL methods, as well as hybrid DL models. The experimental results show that the performance of the proposed method is superior to those of all compared models.
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