Air Pollutant Concentration Prediction Based on a CEEMDAN-FE-BiLSTM Model

Jiang, Xuchu; Wei, Peiyao; Luo, Yang; Li, Ying

doi:10.3390/atmos12111452

Cited by 21 publications

(8 citation statements)

References 26 publications

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“…This method reduces mode aliasing to a certain extent, but it also brings two new problems, namely reconstruction error and increased computational cost (Li et al, 2020). Therefore, Torres et al proposed the CEEMDAN algorithm, which can adaptively add and eliminate Gaussian white noise (Jiang et al, 2021) and obtain subcomponents of different frequencies through iterative decomposition of residues. Compared with EMD and EEMD, CEEMDAAN effectively eliminates mode aliasing, while the reconstruction error is almost zero and the computational cost is reduced (Cao et al, 2018).…”

Section: Data Preprocessing Algorithmmentioning

confidence: 99%

A Combined model based on data decomposition and multi-model weighted optimization for precipitable water vapor forecasting

Zhang

Kou

Ren

et al. 2022

Preprint

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Water shortage is a major problem facing the world. Artificial precipitation enhancement is an effective way to improve precipitation conversion rate, but the selection of artificial precipitation enhancement operation timing is the main difficulty, and the precipitable water vapor(PWV) is a major index. The variation of PWV is nonlinear and unstable due to complex factors, especially in the Qilian mountains in the northeastern part of the Qinghai-Tibet Plateau, so it is difficult to predict it accurately. Therefore, based on the analysis of the observed data of microwave radiometer in Qilian Mountains, a new combined model is constructed which considers both data decomposition and prediction of several single models in this research. In the data preprocessing stage, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is used to decompose and de-noise the PWV sequence. In the prediction stage, four neural network with unique characteristics, back propagation neural network (BPNN), long short term memroy (LSTM), bidirectional gated recurrent unit (BiGRU) and temporal convolutional network (TCN), are selected to predict the decomposed data respectively. A variant of gray Wolf optimization algorithm (IGWO) is used to determine the optimal weight of the model, and finally the comprehensive predicted value is obtained by weighting calculation. Through the analysis of experimental results, in the longest 15-step prediction, compared with CEEMDAN-BP, CEEMDAN-LSTM, CEEMDAN-BiGRU, CEEMDAN-TCN, the prediction accuracy can be improved by 54.17%, 35.05%, 22.38%, 23.86%, respectively. Other step size prediction also achieves the highest prediction accuracy.

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Section: Data Preprocessing Algorithmmentioning

confidence: 99%

A Combined model based on data decomposition and multi-model weighted optimization for precipitable water vapor forecasting

Zhang

Kou

Ren

et al. 2022

Preprint

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show abstract

“…Air pollution forecasting techniques include numerical models and statistical models [ 29 ]. The numerical models achieve the simulation of the transformation and diffusion of air pollutants and reflect the change law of air pollutants.…”

Section: Introductionmentioning

confidence: 99%

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Guo

Wang

2023

Toxics

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Anthropogenic sources of fine particulate matter (PM2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM2.5 concentration in Shanghai. The PM2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM2.5 concentration in Shanghai. The PM2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM2.5 concentration predicting models were the PM2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM2.5 in the next day and the input influence factors, the PM2.5 showed the closest relation with the PM2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.

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“…Zhang et al [ 37 ] proposed and validated the EEMD-LSTM hybrid model as a suitable model for temperature forecasting. Jiang et al [ 38 ] proposed a CEEMDAN-FE-BILSTM hybrid model to predict PM2.5 concentration. Lin et al [ 39 ] proposed a hybrid method combining CEEMDAN and ML-GRU (multi-layer gated recurrent unit) to accurately predict crude oil prices.…”

Section: Introductionmentioning

confidence: 99%

Temperature Prediction of Seasonal Frozen Subgrades Based on CEEMDAN-LSTM Hybrid Model

Chen

Liu

Zeng

et al. 2022

Sensors

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Improving the temperature prediction accuracy for subgrades in seasonally frozen regions will greatly help improve the understanding of subgrades’ thermal states. Due to the nonlinearity and non-stationarity of the temperature time series of subgrades, it is difficult for a single general neural network to accurately capture these two characteristics. Many hybrid models have been proposed to more accurately forecast the temperature time series. Among these hybrid models, the CEEMDAN-LSTM model is promising, thanks to the advantages of the long short-term memory (LSTM) artificial neural network, which is good at handling complex time series data, and its combination with the broad applicability of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) in the field of signal decomposition. In this study, by performing empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and CEEMDAN on temperature time series, respectively, a hybrid dataset is formed with the corresponding time series of volumetric water content and frost heave, and finally, the CEEMDAN-LSTM model is created for prediction purposes. The results of the performance comparisons between multiple models show that the CEEMDAN-LSTM model has the best prediction performance compared to other decomposed LSTM models because the composition of the hybrid dataset improves predictive ability, and thus, it can better handle the nonlinearity and non-stationarity of the temperature time series data.

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Air Pollutant Concentration Prediction Based on a CEEMDAN-FE-BiLSTM Model

Cited by 21 publications

References 26 publications

A Combined model based on data decomposition and multi-model weighted optimization for precipitable water vapor forecasting

A Combined model based on data decomposition and multi-model weighted optimization for precipitable water vapor forecasting

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Temperature Prediction of Seasonal Frozen Subgrades Based on CEEMDAN-LSTM Hybrid Model

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