Journal of South China University of Technology(Natural Science Edition) ›› 2025, Vol. 53 ›› Issue (5): 66-81.doi: 10.12141/j.issn.1000-565X.240356

• Computer Science & Technology • Previous Articles    

Research on Carbon Emission Forecasting in Transportation Industry Based on SD-ISSA-DALSTM

WANG Qingrong1  WANG Junjie1  ZHU Changfeng2  HAO Fule1

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  1. 1.School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China;

    2.School of Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

  • Online:2025-05-25 Published:2024-11-12

Abstract:

Aiming at the problem of high volatility and stochasticity and low prediction accuracy in the carbon emission data series of transportation industry, a transportation carbon emission prediction model combining the quadratic decomposition （SD）, dual attention mechanism （DA）, improved sparrow search algorithm （ISSA） and LSTM network is proposed. First, CEEMDAN is introduced to decompose the transportation carbon emission data series into modal components with different frequencies, and then the sample entropy （SE） is used to quantify the complexity of each component, and the secondary decomposition （SD） is performed on the component with the highest entropy value by using VMD, which further weakens the volatility and nonlinearity of the transportation carbon emission data series; second, in order to tap the correlation between the transportation carbon emission and its influencing factors, a feature-attention mechanism is added to the inputs of the LSTM Secondly, in order to explore the correlation between transportation carbon emissions and its influencing factors, a feature attention mechanism is added to the input side of the model to highlight the key input features; meanwhile, a temporal attention mechanism is added to the output side to extract the key historical moments; finally, the SSA algorithm is improved by combining the Circle chaotic mapping, dynamic inertia weight factor and mixed variance operator strategies, and the ISSA-DALSTM models are established for each component separately, and then reconstructed for the predicted values of each component. The carbon emission data of China's transportation industry from 1990 to 2019 are measured to validate the model, and the results show that the RMSE, MAE, and MAPE of the proposed model are 5.3088, 3.5661, and 0.4439, respectively, which are better than those of other comparative models, and the validity of the proposed model is verified.

Key words: transportation industry, carbon emission prediction, secondary decomposition, dual attention mechanism, improved sparrow search algorithm