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
DOI :
https://doi.org/10.38124/IJISRT21DEC018
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
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.
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.
