Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), a...

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Bibliographic Details
Main Authors:	Pande C.B., Kushwaha N.L., Alawi O.A., Sammen S.S., Sidek L.M., Yaseen Z.M., Pal S.C., Katipo?lu O.M.
Other Authors:	57193547008
Format:	Article
Published:	Elsevier Ltd 2025
Subjects:	Air Pollutants Air Pollution Cities Environmental Monitoring Forecasting India Neural Networks, Computer Seasons Delhi Air quality Decision making Learning systems Mean square error Quality assurance Regression analysis Air quality indices Bidirectional recurrent neural networks Decisions makings Deep learning model Delhi air pollution Kernel ridge regressions Learning models Performance Recurrent neural network model Urban cities air quality artificial neural network atmospheric pollution forecasting method index method model urban area air pollution article case study decision making deep learning forecasting human machine learning nerve cell network recurrent neural network ridge regression root mean squared error short term memory winter air pollutant city environmental monitoring procedures season Long short-term memory
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Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

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