Air Quality Index Forecasting Using RNN and LSTM


Authors : Rahul Singh; Satyam Sagar; Hitesh Kumar; Vaibhav Rai

Volume/Issue : Volume 6 - 2021, Issue 12 - December

Google Scholar : http://bitly.ws/gu88

Scribd : https://bit.ly/3drjvh7

Nowadays AIR Pollution is a great concern and totally dominant topic in this world of industrialization. This needs to be controlled by predicting some future predicting techniques like AQI. AQI decides whether whether the air is pure or having hazardous air quality. AQI follows the regular pattern which is prominent in predicting the future air pollution quality. AQI follows the regular pattern which is prominent in predicting the future air pollution quality. LSTM is a relevant RNN technique which plays a crucial role in these deep learning methods. LSTM is used in time-series forecasting problems in which the inputs are taken in the form of regression values and can be predicted by using graphical techniques . This paper consist of deep information regarding RNN and LSTM which proved to be helpful is predicting the air pollution quality. This paper gives a serious impact of particulate matter (as pm2.5) in elaborating the data of particular city for some span of time

Keywords : LSTM Cell , RNN, , Air Quality Index(AQI), Regression Analysis , PM 2.5.

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