A Deep Long Short-Term Memory Network for the Prediction of Particulate Matter PM 2.5 Concentration

Abstract

Forecasting particulate matter of size less than 2.5 micrometers PM 2.5 in big cities is a major challenge for scientific community. In addition to environmental impacts, these particulate matter cause various diseases, such as cardiopulmonary disease, stroke, lung cancer and even neurological disorders. Forecasting high PM 2.5 events helps to raise awareness among people to take precautionary measures, such as limit outdoor activities and use of masks, etc. In future, advanced Machine Learning (ML) based PM 2.5 forecasting will help to reduce the cost of sampling of PM 2.5, such as samplers and equipment costs, which are needed to measure the concentration of particulate matter in air. Air pollution not only damages the environment but also leads to various illnesses such as respiratory tract and cardiovascular diseases. Nowadays, estimating air pollutants concentration is becoming very important so that people can prepare themselves for the hazardous impact of air pollution beforehand. Various deterministic models have been used to forecast air pollution. In this study, along with various pollutants and meteorological parameters, we also use the concentration of the pollutants predicted by the community multiscale air quality (CMAQ) model which are strongly related to PM 2.5 concentration. After combining these parameters, we implement various machine learning models to predict the hourly forecast of PM 2.5 concentration in two big cities of South Korea and compare their results. It has been shown that Long Short Term Memory network outperforms other well-known gradient tree boosting models, recurrent and convolutional neural networks.