Comparative Study on Predicting Particulate Matter (PM2.5) Levels Using LSTM Models

Abstract

In recent times, air pollution has attracted the attention of policymakers and researchers as an important issue. The pollution that contaminates the air that people breathe is from pollutants such as oxides of carbon, nitrogen and sulphur as well minuscule dust particle which are smaller than 0.0025mm in diameter. The emissions contain many substances that are harmful to human health when exposed to them for a prolonged period or more than certain levels of concentration. The recent advent of technology in sensors and compact instruments to measure the concentration of pollutant levels with considerable ease. Further, this paper also predicts the air pollution for using multiple Deep Learning models that are variations of the Long Short-Term Memory (LSTM) model. In this research, only PM2.5 alone taken into consideration for prediction. Real-time air quality data were collected at selected places in the study area. It is found that the model prediction data is well matched with the other researchers' results and real-time data.