Air Quality Index Prediction

Air Quality Index Prediction

Dr Kanmani P¹, Sarthak Gupta², Gurleen Kaur³

¹Department of Data Science and Business Systems, SRM Institute of Science and Technology, Chennai, India

² Department of Data Science and Business Systems, SRM Institute of Science and Technology, Chennai, India

³ Department of Data Science and Business Systems, SRM Institute of Science and Technology, Chennai, India

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Abstract - We address air index forecast by inferring, using deep and machine learning algorithms, the concentration per hour of air pollutants (such ozone, Particulate Matter (2.5), and Sulphur). DL is one of the most popular methods, may efficiently apply optimization algorithms to train a model on large amounts of information and data. The air quality prediction model is trained using time series data. In our approach, the sequence module makes use of deep CNN, and time series data is progressively fed into the CNN model for training. In CNN, there are many different functioning layers, including RNN, and LSTM. The CNN layer can be utilized to successfully extract the features that are sequential of time series. Advanced features outperform normal features when analyzing data of time series. CNN's pooling layer oversees down sampling. CNN is good at forecasting air quality, according to test data.

Key Words:  AQP, CNN, PM, RNN, DL