Analyzing area-wise air pollution level using machine learning for a better future

Abstract

Environment consists of nature and surroundings where all living beings co-exist. Harming the environment will in turn harm all living and non-living things alike. One of the major concerns of environment pollution is air pollution, which affects human health, vegetation and aquatic life. However, in developing countries like Bangladesh, air pollution is not considered a major issue. It is mostly caused by the release of harmful gases into the atmosphere. Our goal is to develop a model using machine learning which will determine the level of air pollution in a particular area, detect elements which cause air pollution and predict future pollution level. Algorithms such as Linear Regression, Facebook Prophet, RNN and ARIMA models have been used throughout the course of this study. From RNN we have used LSTM model for prediction which uses special units as well as standard units. With these models we have predicted the pollutant emission rate for analyzing the area-wise pollution rate. We have used different type of algorithms to successfully get the optimum result and to get the fi nal result with less error. This will help to analyze the overall air pollution condition which will help to take necessary steps accordingly.