Carbon Emission Prediction in Transportation Industry Based on SD-ISSA-DALSTM

Aiming at the low accuracy of carbon emission prediction caused by the high volatility and nonlinearity of the carbon emission data series in transportation industry, a transportation carbon emission prediction model combining the secondary decomposition, dual attention mechanism, improved sparrow search algorithm (ISSA) and long short-term memory (LSTM) network is proposed. First, complete ensemble empirical mode decomposition with adaptive noise is introduced to decompose the transportation carbon emission data series into modal components with different frequencies, then sample entropy is used to quantify the complexity of each component, and secondary decomposition is performed on the component with the highest entropy value via variational mode decomposition, which further weakens the volatility and nonlinearity of the transportation carbon emission data series. Next, in order to explore the correlation between transportation carbon emission and its influencing factors, a double attention mechanism-optimized LSTM (DALSTM) model is constructed, in which a feature attention mechanism is added to the input side of the LSTM 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, the dynamic inertia weight factor and the mixed variance operator strategies, ISSA-DALSTM models are established for each component separately, and the predicted values of each component are reconstructed. By measuring the carbon emission data of China's transportation industry from 1990 to 2019, it is found that the root mean square error, mean square error, and mean absolute percentage error of the proposed model are respectively 5.3088, 3.5661 and 0.4439, which are better than those of other comparative models, thus verifying the validity of the proposed model.