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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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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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spelling	my.uniten.dspace-365402025-03-03T15:42:58Z Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India 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. 57193547008 57219726089 56108584300 57192093108 35070506500 56436206700 57208776491 57203751801 Air Pollutants Air Pollution Cities Environmental Monitoring Forecasting India Neural Networks, Computer Seasons Delhi India Air quality Decision making Forecasting 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 air quality 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 artificial neural network city environmental monitoring India procedures season Long short-term memory 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), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city. ? 2024 Elsevier Ltd Final 2025-03-03T07:42:58Z 2025-03-03T07:42:58Z 2024 Article 10.1016/j.envpol.2024.124040 2-s2.0-85192841115 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192841115&doi=10.1016%2fj.envpol.2024.124040&partnerID=40&md5=fed452b3bfa8d1a007a15fdd4aa69a3d https://irepository.uniten.edu.my/handle/123456789/36540 351 124040 Elsevier Ltd Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Air Pollutants Air Pollution Cities Environmental Monitoring Forecasting India Neural Networks, Computer Seasons Delhi India Air quality Decision making Forecasting 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 air quality 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 artificial neural network city environmental monitoring India procedures season Long short-term memory
spellingShingle	Air Pollutants Air Pollution Cities Environmental Monitoring Forecasting India Neural Networks, Computer Seasons Delhi India Air quality Decision making Forecasting 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 air quality 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 artificial neural network city environmental monitoring India procedures season Long short-term memory 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. Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India
description	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), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city. ? 2024 Elsevier Ltd
author2	57193547008
author_facet	57193547008 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.
format	Article
author	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.
author_sort	Pande C.B.
title	Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India
title_short	Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India
title_full	Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India
title_fullStr	Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India
title_full_unstemmed	Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India
title_sort	daily scale air quality index forecasting using bidirectional recurrent neural networks: case study of delhi, india
publisher	Elsevier Ltd
publishDate	2025
_version_	1825816186571456512
score	13.244413

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

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