A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring

Abstract

Nowadays, air pollution is a big environmental problem in developing countries. In this problem, particulate matter 2.5 (PM2.5) in the air is an air pollutant. When its concentration in the air is high in developing countries like Vietnam, it will harm everyone's health. Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen. This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City, Vietnam. Firstly, this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC dataset. Only variables that affect the results will be selected for PM2.5 concentration prediction. Secondly, an efficient PM25-CBL model that integrates a convolutional neural network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) is developed. This model consists of three following modules: CNN, Bi-LSTM, and Fully connected modules. Finally, this study conducts the experiment to compare the performance of our approach and several state-of-the-art deep learning models for time series prediction such as LSTM, Bi-LSTM, the combination of CNN and LSTM (CNN-LSTM), and ARIMA. The empirical results confirm that PM25-CBL model outperforms other methods for Air Quality HCMC dataset in terms of several metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).