Air quality prediction based on supervised machine learning methods

Abstract

Generally, Air pollution alludes to the issue of toxins into the air that are harmful to human well being and the entire planet. It can be described as one of the most dangerous threats that the humanity ever faced. It causes damage to animals, crops, forests etc. To prevent this problem in transport sectors have to predict air quality from pollutants using machine learning techniques. Subsequently, air quality assessment and prediction has turned into a significant research zone. The aim is to investigate machine learning based techniques for air quality prediction. The air quality dataset is preprocessed with respect to univariate analysis, bi-variate and multi-variate analysis, missing value treatments, data validation, data cleaning/preparing. Then, air quality is predicted using supervised machine learning techniques like Logistic Regression, Random Forest, K-Nearest Neighbors, Decision Tree and Support Vector Machines. The performance of various machine learning algorithms is compared with respect to Precision, Recall and F1 Score. It is found that Decision Tree algorithm works well for predicting air quality. This application can help the meteorological Department in predicting air quality. In future, this work can be optimized by applying Artificial Intelligence techniques.