Abstract
Air pollution is a major problem in many countries and especially in
India. In November 2019, New Delhi recorded an air quality index level
of 900, which is considered higher than the ‘severe’ level. The air
pollution forecasting method predicts the pollution based on the
available dataset and the data features and certain method performance
in forecasting at high accuracy depends on the method and the measures
used. Forecasting air quality levels in countries like India is very
important because it has a direct impact on public health, and thus, is
used for decision making. The main goal for this paper is to investigate
air quality index prediction based on different algorithms, so experts
can identify the methods that require development and it is useful as a
starting point for novice researchers. The problem of the Air Quality
Index (AQI) prediction in this paper is approached with different Fuzzy
Inference Systems (FIS), Neural Networks, Swarm intelligence techniques
and so on. The results of the experiments shows that the LSTM model
performs better than all the deep learning based models and Fuzzy based
models discussed in this paper