An Ensemble Deep Learning Model for Forecasting Hourly PM2.5 Concentrations

Abstract

Air pollution has become an environmental threat globally. Therefore, air pollution estimation and prediction are crucial for effecting timely control measures. This paper proposes an ensemble deep particulate forecaster (EDPF) to predict hourly PM2.5 concentrations for a day ahead. Air quality data from Thiruvananthapuram, Kerala (an Indian state), is used as a case study to demonstrate the model. The effect of other pollutant concentrations and various meteorological parameters on PM2.5 prediction is first studied using Pearson correlation analysis and recursive feature elimination method. Thus, the most relevant features for the forecast of PM2.5 concentrations are identified. Then preliminary PM2.5 predictions are made using two base predictors: an attention-based convolutional neural network - bidirectional long short-term memory (CNN-BiLSTM) model and a bidirectional long short-term memory model (BiLSTM). Finally, a feed-forward deep neural network (DNN) model combines the preliminary predictions yielding the final PM2.5 predictions. The proposed model is validated for long-term forecasting of PM2.5 at Thiruvananthapuram, and the model attains root mean squared error of 12.96 µg/m3 and mean absolute error of 9.28 µg/m3 for forecasting 24 h ahead PM2.5 concentrations. The experimental results demonstrate that the model performs better compared to various deep learning models in the literature. The effectiveness of the model in all four seasons is also evaluated, which reveals winter is the most challenging season to forecast.