2021
DOI: 10.3389/fenvs.2021.752318
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Modelling Particulate Matter Using Multivariate and Multistep Recurrent Neural Networks

Abstract: Air quality is a major problem in the world, having severe health implications. Long-term exposure to poor air quality causes pulmonary and cardiovascular diseases. Several studies have also found that deteriorating air quality also causes substantial economic losses. Thus, techniques that can forecast air quality with higher accuracy may help reduce health and economic consequences. Prior research has utilized state-of-the-art artificial neural network and recurrent neural network models for forecasting air q… Show more

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Cited by 3 publications
(2 citation statements)
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“…The Takagi-Sugeno FIS provides the foundation for an ANFIS or adaptable network-based FIS. In the early 1990s, this method was created [19][20][21]. It can combine the advantages of NNs and FL into a single framework because of the way it incorporates both.…”
Section: Adaptive Neuro Fis (Anfis)mentioning
confidence: 99%
“…The Takagi-Sugeno FIS provides the foundation for an ANFIS or adaptable network-based FIS. In the early 1990s, this method was created [19][20][21]. It can combine the advantages of NNs and FL into a single framework because of the way it incorporates both.…”
Section: Adaptive Neuro Fis (Anfis)mentioning
confidence: 99%
“…In recent years, various deep learning models have also been successfully employed for air quality PM2.5 prediction (Liao et al, 2020;Aggarwal and Toshniwal, 2021;Saini et al, 2021;Seng et al, 2021;Zaini et al, 2021). In particular, Ragab et al, presented a method of air pollution index (AQI) prediction by means of using one-dimensional convolutional neural network (1D-CNN) and exponential adaptive gradients optimization for Klang city, in Malaysia (Ragab et al, 2020).…”
Section: Introductionmentioning
confidence: 99%