School of Vehicle and Energy, Yan Shan University, Qinhuangdao, 066004, China; School of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
School of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
J Environ Manage. 2021 Jul 1;289:112438. doi: 10.1016/j.jenvman.2021.112438. Epub 2021 Apr 16.
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.
小波变换（WT）是一种先进的预处理技术，已广泛应用于 PM10 预测。然而，由于小波层数的经验选择，该技术的性能不稳定。为了在 PM10 预测中确定最优的小波层数，提出了一种基于 WT、长短时记忆（LSTM）和堆叠自动编码器（SAE）的创新耦合模型。本研究通过小波分解设计了一个包含 960 个高低频分量的交叉实验，并基于来自不同区域的 12 个样本，用 SAE-LSTM 预测每个分量。结果表明，与其他双向 LSTM（BiLSTM）和基于一些误差评估指标（即纳什-苏特克里夫效率系数（NSEC））的 LSTM 相比，所开发的模型表现更好，与其他步骤相比，在均方根误差（RMSE）方面，两步预测的精度最高。此外，对于 12 个样本，对其进行分解时采用高层的预测精度高于采用低层的预测精度。本文确定了 PM10 预测中最优的小波层数，这为基于 WT 应用的其他预测场景提供了有意义的参考。
