A Survey and Performance Review on Air Quality Data Prediction Techniques in Deep Learning Frameworks


Authors : K.Sathya; Dr.T.Ranganayaki

Volume/Issue : Volume 7 - 2022, Issue 8 - August

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3DqGLKk

DOI : https://doi.org/10.5281/zenodo.7082027

With increased industry and urbanization, air pollution is becoming an environmental hazard. Air Quality (AQ) is becoming increasingly important for both the environment and humanity. The atmosphere contaminations that cause air pollution like CO2, NO2, etc., are produced by the combustion of natural gas, coal and wood, as well as by industry and cars. Air pollution may cause serious diseases such as lung cancer, brain damage, and even death. So, predicting AQ is an important step for the government to take because it is becoming a big problem for human health. To predict AQ, many artificial intelligence frameworks have been developed over earlier days using the different historical data on air pollutants in various regions. This manuscript covers a complete study of various Deep Learning (DeepLearn) frameworks developed to forecast AQ using the different available air pollution databases. First, different AQ prediction frameworks relying on the DeepLearn structures are discussed briefly. After that, a comparative study is conducted to understand the drawbacks of those frameworks and suggest a new solution to predict the AQ accurately.

Keywords : Air pollution, Air pollutants, Air quality, Forecasting, Artificial intelligence, DeepLearn.

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