Predicting air quality in smart city using novel transfer learning based framework

Shilpa Sonawani, Kailas Patil


Air quality is a matter of concern these days due to its adverse effect on human health. Multiple new air pollution monitoring and prediction stations are being developed in smart cities to tackle the issue. Recent advanced deep learning techniques show excellent performance for air quality predictions but need sufficient training data for model performance. The data insufficiency issue at a new station can be resolved using the proposed novel transfer learning-based framework to predict pollution concentration at the new station. The prediction ability at a new station can be significantly enhanced by this effective technology. The performance of the model is assessed on various stations in Delhi, India.


Air quality; Chaining approach; Deep learning; Multi-headed CNN-GRU; Smart city and safety; Transfer learning

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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