Abstract

Air pollution is one of the principal environmental issues for the industrial emission and infection of the atmosphere which is caused by the climatic and traffic elements, burning of fossil fuels, etc. For the past several years, various methods and models have been discovered to detect the pollution of the air. In this paper, among all of those, three mechanisms have been focused, which are image processing approach, machine learning, and deep learning technique. A comparative study has developed among these three methods to detect the pollutant of the air in the account of time, cost and efficiency so that different scenario and system can choose the best method according to their need. The objective of this paper is to assimilate the procedure of these methods in brief and utilize this study to estimate the best solution for the corresponding requirement of any particular circumstances.

How to Cite
SULTANA, Samia. A Comparison Study of Air Pollution Detection using Image Processing, Machine Learning and Deep Learning Approach. Global Journal of Computer Science and Technology, [S.l.], sep. 2019. ISSN 0975-4172. Available at: <https://computerresearch.org/index.php/computer/article/view/1867>. Date accessed: 19 oct. 2019.