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oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/117392026-10-18T21:13:17Zopenairedrivercom_ebyu_10000col_ebyu_10001

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, IndiaPande, Chaitanya BaliramKushwaha, Nand LalAlawi, Omer A.Sammen, Saad ShSidek, Lariyah MohdYaseen, Zaher MundherPal, Subodh ChandraKatipoğlu, Okan MertAir quality indexDeep learning modelDelhi air pollutionUrban citiesAir PollutantsAir PollutionCitiesEnvironmental MonitoringForecastingIndiaNeural Networks, ComputerSeasonsDelhiAir qualityDecision makingLearning systemsMean square errorQuality assuranceRegression analysisAir quality indicesBidirectional recurrent neural networksDecisions makingsKernel ridge regressionsLearning modelsPerformanceRecurrent neural network modelartificial neural networkatmospheric pollutionforecasting methodindex methodThis 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 LtdElsevier Ltd2024Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/11739https://doi.org/10.1016/j.envpol.2024.1240400269-7491enEnvironmental Pollutioninfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/11739