An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration. (1st July 2021)

• Record Type: Journal Article
• Authors: Qiao, Weibiao; Wang, Yining; Zhang, Jianzhuang; Tian, Wencai; Tian, Yu; Yang, Quan
• Is Part Of: Journal of environmental management. Volume 289(2021)
• Publication Date: 2021-07-01
• Subjects: PM10 -- Long short-term memory -- Stacked autoencoder -- Wavelet transform -- Prediction; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2021.112438
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Wavelet transform (WT) is an advanced preprocessing technique, which has been widely used in PM 10 prediction. However, this technique cannot provide stable performance due to the empirical selection of wavelet's layers. For fixing the optimal wavelet's layers in PM10 forecasting, an innovative coupled model based on WT, long short-term memory (LSTM), and SAE (stacked autoencoder) are proposed. This study designs a crossover experiment with 960 high- and low-frequency components by wavelet decomposition and predicts each component with SAE-LSTM based on 12 samples from different regions. The results indicate that the developed model outperforms other BiLSTM (Biredictional LSTM) and LSTM based on some error evaluation indicators (i.e. Nash-Sutcliffe efficiency coefficient (NSEC)), and compared with other steps, the accuracy of two-step prediction is the highest in view of root mean squares error (RMSE). In addition, for 12 samples, the prediction accuracy by using high layers is higher than that by adopting low layers for decomposing them. This paper fixes the optimal wavelet' layers in PM10 prediction, which provides a meaningful reference in other prediction scenarios based on the application of WT. Highlights: A novel WT-SAE-LSTM is proposed to forecast PM10 based on twelve samples in China. Compared with the advanced machine learning algorithms (e.g., BiLSTM), SAE-LSTM has better forecasting performance. The optimal wavelet's layers are determined for different samples.