Air Quality Index prediction using machine learning for Ahmedabad city

Prediction of air pollution index may help in traffic routing and identifying serious pollutants. Modeling of the complex relationships between these variables by sophisticated methods in machine learning is a promising field. The objective of this work is to compare the various machine learning methods such as SARIMA, SVM and LSTM for the prediction of air quality index for Ahmedabad city of Gujarat, India. In this research, different preprocessing methods are used to manage the data before providing to the machine learning models. This study is carried out based on the data provided by the Central Pollution Control Board of India and it focuses on the support vector machine algorithm with RBF kernel model. So, that the results availed are comparatively better as compared to other kernels of the support vector machine models as well as SARIMA and LSTM models for Ahmedabad city.