ArticleScientific reports2023
A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Hybrid deep learning model for air quality prediction and its impact on healthcare.Scientific reports · 2026Article
- RETRACTED: LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans.PloS one · 2025Article
- Article
- Challenges in data-driven geospatial modeling for environmental research and practice.Nature communications · 2024Review
- Hybrid FCMG-OP-FIS model approach to convert regression into classification data for machine learning-based AQI prediction.Heliyon · 2024Article
- Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic.PLoS computational biology · 2023Article
- Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images.Diagnostics (Basel, Switzerland) · 2023Article
- High-dimensional spatiotemporal visual analysis of the air quality in China.Scientific reports · 2023Article
- Application of hybrid fuzzy interval-based machine learning models on financial time series - A case study of Taiwan biotech index during the epidemic period.Frontiers in artificial intelligence · 2023Article
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5 authors.
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Abstract
China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as - 25.88 in Wuhan and - 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities.
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