Abstract:
In this paper, it is aimed to predict the Air Quality Index (AQI) by the use of Machine learning algorithms.
To reach this, the key parameters have been selected which can affect the Air quality index are temperature,
humidity, pressure, wind speed, PM10 and SO2 respectively. Air quality of certain states in India can be used
as one of the major factors determining pollution index also how well the city's industries and population is
controlled. Urbanized Air quality monitoring has been a constant challenge with the advent of
industrialization. Air pollution causes conspicuous damage to the environment as well as to human health
resulting in acid rain, heart diseases, global warming and skin cancer to all humankind. This paper addresses
the challenge of predicting the Air Quality Index (AQI), with the goal to reduce the pollution before it gets
unfavourable and also suggests mankind to move places in advance, using ensemble techniques for predicting
the Air Quality Index (AQI). This paper investigates how effective some available prediction models are in
predicting the Air Quality Index (AQI) values provided some input data, based on the pollution and
meteorological information in India. We carry out regression analysis on the dataset, and our results shows
which meteorological factors impact the AQI values most and how helpful the predictive models are to help
in air quality prediction. |
