PM2.5 forecasting for an urban area based on deep learning and decomposition method

Abstract

Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

“…Hybrid machine-learning algorithms have been extensively developed in the last decade for predicting PM 2.5 concentrations 20 – 22 . These models have several advantages over standalone machine learning models for forecasting PM 2.5 concentrations.…”

Deep-learning architecture for PM2.5 concentration prediction: A review

“…[ 84 ] 2020 Beijing, China EMD-CNN-GRU H/M/T+(1-24) 42.26 34.95 65.30 0.67 Zaini et al. [ 85 ] 2022 Cheras/Batu Muda, Malaysia EEMD-LSTM H/S/T+1 4.21/4.89 2.81/2.77 14.15/14.64 0.97/0.96 Zhang et al. [ 86 ] 2021 Beijing, China VMD-BiLSTM H/S/T+1 9.39 5.35 16.40 0.99 Chang et al.…”

“…Zaini et al. [ 85 ] applied EEMD to decompose the input data for LSTM, while Liu et al. [ 89 ], Fu et al.…”

Long-Term Forecasting of Air Pollution Particulate Matter (PM2.5) and Analysis of Influencing Factors

“…This technique is categorized into twotypes based on the network structure: Artificial Neural Networks (ANNs) and Recurrent Neural Networks (RNNs). These methods find significant applications in domains like long-term PM2.5 forecasting [27][28][29][30][31], where nonlinear relationships and patterns are prevalent. By employing ensemble learning and deep learning, researchers and practitioners can avoid the problem of nonlinear and non-stationary data in these fields more effectively.…”