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

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dc.citedby5
dc.contributor.authorPande C.B.en_US
dc.contributor.authorKushwaha N.L.en_US
dc.contributor.authorAlawi O.A.en_US
dc.contributor.authorSammen S.S.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorYaseen Z.M.en_US
dc.contributor.authorPal S.C.en_US
dc.contributor.authorKatipo?lu O.M.en_US
dc.contributor.authorid57193547008en_US
dc.contributor.authorid57219726089en_US
dc.contributor.authorid56108584300en_US
dc.contributor.authorid57192093108en_US
dc.contributor.authorid35070506500en_US
dc.contributor.authorid56436206700en_US
dc.contributor.authorid57208776491en_US
dc.contributor.authorid57203751801en_US
dc.date.accessioned2025-03-03T07:42:58Z
dc.date.available2025-03-03T07:42:58Z
dc.date.issued2024
dc.description.abstractThis 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 Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo124040
dc.identifier.doi10.1016/j.envpol.2024.124040
dc.identifier.scopus2-s2.0-85192841115
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85192841115&doi=10.1016%2fj.envpol.2024.124040&partnerID=40&md5=fed452b3bfa8d1a007a15fdd4aa69a3d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36540
dc.identifier.volume351
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleEnvironmental Pollution
dc.subjectAir Pollutants
dc.subjectAir Pollution
dc.subjectCities
dc.subjectEnvironmental Monitoring
dc.subjectForecasting
dc.subjectIndia
dc.subjectNeural Networks, Computer
dc.subjectSeasons
dc.subjectDelhi
dc.subjectIndia
dc.subjectAir quality
dc.subjectDecision making
dc.subjectForecasting
dc.subjectLearning systems
dc.subjectMean square error
dc.subjectQuality assurance
dc.subjectRegression analysis
dc.subjectAir quality indices
dc.subjectBidirectional recurrent neural networks
dc.subjectDecisions makings
dc.subjectDeep learning model
dc.subjectDelhi air pollution
dc.subjectKernel ridge regressions
dc.subjectLearning models
dc.subjectPerformance
dc.subjectRecurrent neural network model
dc.subjectUrban cities
dc.subjectair quality
dc.subjectartificial neural network
dc.subjectatmospheric pollution
dc.subjectforecasting method
dc.subjectindex method
dc.subjectmodel
dc.subjecturban area
dc.subjectair pollution
dc.subjectair quality
dc.subjectarticle
dc.subjectcase study
dc.subjectdecision making
dc.subjectdeep learning
dc.subjectforecasting
dc.subjecthuman
dc.subjectmachine learning
dc.subjectnerve cell network
dc.subjectrecurrent neural network
dc.subjectridge regression
dc.subjectroot mean squared error
dc.subjectshort term memory
dc.subjectwinter
dc.subjectair pollutant
dc.subjectartificial neural network
dc.subjectcity
dc.subjectenvironmental monitoring
dc.subjectIndia
dc.subjectprocedures
dc.subjectseason
dc.subjectLong short-term memory
dc.titleDaily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, Indiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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