Abstract
High concentration PM2.5 may cause serious damage to human health. Accurate PM2.5 concentration forecasting can provide the public with timely and effective PM2.5 pollution warning information. In the current mainstream studies, most existing air pollution forecasting models use only one predictor, of which the accuracy and stability can be further improved. In this study, a novel hybrid ensemble model with three deep learning predictors is proposed for hourly PM2.5 concentration forecasting. In the proposed model, the complementary ensemble empirical mode decomposition (CEEMD) is used to extract the features in the PM2.5 data series to reduce its complexity. Three deep neural networks are used as predictors for data forecasting, including deep belief network (DBN), long short-term memory network (LSTM), and multilayer perceptron (MLP). Each predictor is given a weight, and the imperial competition algorithm (ICA) is used to optimize weights to obtain the best forecasting result. Two groups of PM2.5 concentration data from Shanghai are used to validate the model. The experimental results show that the proposed model has high accuracy and robustness, and can outperform all comparison models.
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Abbreviations
- ARMA:
-
autoregressive integrated moving average
- BEMD:
-
bivariate empirical mode decomposition
- BP:
-
back propagation
- CEEMD:
-
complementary ensemble empirical mode decomposition
- DBN:
-
deep belief network
- ENN:
-
Elman neural network
- GA:
-
genetic algorithm
- ICA:
-
imperial competition algorithm
- LSTM:
-
long short-term memory network
- MAE:
-
mean absolute error
- MAPE:
-
mean absolute percentage error
- MARS:
-
multivariate adaptive regression splines
- MLP:
-
multilayer perceptron
- MLPI:
-
utilized interval multilayer perceptron
- PSO:
-
particle swarm optimization
- r:
-
Pearson correlation coefficient
- RBM:
-
restricted Boltzmann machine
- RMSE:
-
root mean square error
- RNN:
-
recurrent neural network
- SVR:
-
support vector regression
- WT:
-
wavelet decomposition
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Acknowledgments
The study is fully supported by the National Natural Science Foundation of China (61873283), the Changsha Science & Technology Project (KQ1707017) and the Innovation Driven Project of the Central South University (2019CX005).
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Liu, H., Dong, S. A novel hybrid ensemble model for hourly PM2.5 forecasting using multiple neural networks: a case study in China. Air Qual Atmos Health 13, 1411–1420 (2020). https://doi.org/10.1007/s11869-020-00895-7
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DOI: https://doi.org/10.1007/s11869-020-00895-7