Exploratory Analysis and Feature Selection for the Prediction of Nitrogen Dioxide

Nitrogen dioxide is one of the most hazardous pollutants identified by the World Health Organisation. Predicting and reducing pollutants is becoming a very urgent task and many methods have been used to predict their concentration, such as physical or machine learning models. In addition to choosing the right model, it is also critical to choose the appropriate features. This work focuses on the spatiotemporal prediction of nitrogen dioxide concentration using Bidirectional Convolutional LSTM integrated with the exploration of nitrogen dioxide and associated features, as well as the implementation of feature selection methods. The Root Mean Square Error and the Mean Absolute Error were used to evaluate the proposed approach.