UN and many countries are calling for urgent measures because air pollution caused by air pollutants seriously threatens human health and causes premature death. Many investigations and studies have revealed that PM2.5 is a main pollutant that destroys the environment of ecosystems and harms human health, and that air pollutants containing PM 2.5 cause various diseases such as respiratory, lung and heart diseases.
In the air pollution concentration prediction task, it was reported that the artificial neural network (ANN) model improves prediction performance while avoiding the complexity and annoyance of modeling, compared to the deterministic method which has lack in representing the heterogeneity and nonlinearity of many factors related to pollutant formation.
Therefore, many researchers have begun to apply artificial neural networks extensively to air pollution prediction, and since then they have ensured the accurate prediction performance by combining deep neural networks with several other optimization techniques that have shown dramatic effectiveness in the prediction of large time series data. In a word, their research results show that deep learning models significantly improve their performance when combined with several other effective analytical methods on time series data rather than being used as a single model.
Based on this analysis, Pak Un Jin, a researcher at the Faculty of Automatics, proposed a hybrid model consisting of convolutional neural network (CNN)-long short-term (LSTM) with multifractal detrended fluctuation analysis (MF-DFA) for air pollution and meteorological time series data, and used it to predict the next day’s 24-h average PM2.5 concentration in Beijing City.
The comparison of the performance indexes of the proposed model with MLP and LSTM models showed that the proposed model provides higher prediction accuracy.
For further details, please refer to his paper “A deep learning approach via multifractal detrended fluctuation analysis for PM2.5 prediction” in “Journal of Atmospheric and Solar-Terrestrial Physics” (SCI).