ArticleProcess safety and environmental protection : transactions of the Institution of Chemical Engineers, Part B2023
A hybrid model for daily air quality index prediction and its performance in the face of impact effect of COVID-19 lockdown.
Article in Process safety and environmental protection : transactions of the Institution of Chemical Engineers, Part B, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Particulate matter (PM2.5 and PM10) prediction using fourier series decomposition in combination with LSTM and SVM.Scientific reports · 2026Article
- Ensemble stacking of machine learning models for air quality prediction for Hyderabad city in India.iScience · 2025Article
- Traffic Flow Prediction in 5G-Enabled Intelligent Transportation Systems Using Parameter Optimization and Adaptive Model Selection.Sensors (Basel, Switzerland) · 2024Article
- Optimized machine learning model for air quality index prediction in major cities in India.Scientific reports · 2024Article
- Recent progress of particle electrode materials in three-dimensional electrode reactor: synthesis strategy and electrocatalytic applications.Environmental science and pollution research international · 2024Review
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2 authors.
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Abstract
Accurate and dependable air quality forecasting is critical to environmental and human health. However, most methods usually aim to improve overall prediction accuracy but neglect the accuracy for unexpected incidents. In this study, a hybrid model was developed for air quality index (AQI) forecasting, and its performance during COVID-19 lockdown was analyzed. Specifically, the variational mode decomposition (VMD) was employed to decompose the original AQI sequence into some subsequences with the parameters optimized by the Whale optimization algorithm (WOA), and the residual sequence was further decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On this basis, a deep learning method bidirectional long short-term memory coupled with added time filter layer and attention mechanism (TFA-BiLSTM) was employed to explore the latent dynamic characteristics of each subsequence. This WOA-VMD-CEEMDAN-TFA-BiLSTM hybrid model was used to forecast AQI values for four cities in China, and results verified that the accuracy of the hybrid model outperformed other proposed models, achieving R
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