Volume & Issue no: Volume 8, Issue 5, September - October 2019
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Title: |
Air Quality Index Prediction of Delhi using LSTM |
Author Name: |
Mohit Bansal, Anirudh Aggarwal, Tanishq Verma, Apoorvi Sood |
Abstract: |
Abstract:Air pollution is one of the most severe problems of
the current time. It is growing day by day because of the vast
level of industrialization and urbanization, causing massive
damage to flora and fauna of the planet. Every moment, we are
breathing air that is full of pollutants, going to our lungs,
impregnating our blood and then the whole body, causing
uncountable health problems. Both state and central
governments have put in many efforts to keep air pollution
under control.
The proposed paper discusses an efficient approach towards the
prediction of air quality index (AQI) of Delhi, India. AQI is a
measure of air quality. It is used to inform citizens about the
associated health impacts of air pollution exposure. So, we
modelled a deep recurrent neural network (RNN) based on
Long-Short Term Memory (LSTM) to predict hourly based
concentrations of pollutants. These concentrations are then used
to calculate AQI. The proposed LSTM model achieved good
results in estimating hourly based ambient air quality. |
Cite this article: |
Mohit Bansal, Anirudh Aggarwal, Tanishq Verma, Apoorvi Sood , "
Air Quality Index Prediction of Delhi using LSTM " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 8, Issue 5, September - October 2019 , pp.
059-068 , ISSN 2278-6856.
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