华南理工大学学报（自然科学版）2025，Vol.53Issue(5)：66-81,16.DOI:10.12141/j.issn.1000-565X.240356
基于SD-ISSA-DALSTM的交通运输业碳排放预测
Carbon Emission Prediction in Transportation Industry Based on SD-ISSA-DALSTM
摘要
Abstract
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 5.308 8,3.566 1 and 0.443 9,which are better than those of other comparative models,thus verifying the validity of the proposed model.关键词
交通运输业/碳排放预测/二次分解/双重注意力机制/改进麻雀搜索算法Key words
transportation industry/carbon emission prediction/secondary decomposition/dual attention mechanism/improved sparrow search algorithm分类
信息技术与安全科学引用本文复制引用
王庆荣,王俊杰,朱昌锋,郝福乐..基于SD-ISSA-DALSTM的交通运输业碳排放预测[J].华南理工大学学报（自然科学版）,2025,53(5):66-81,16.基金项目
国家自然科学基金项目(72161024) （72161024）
甘肃省教育厅"双一流"重大研究项目(GSSYLXM-04) Supported by the National Natural Science Foundation of China(72161024)and the"Double-First Class"Ma-jor Research Programs of the Educational Department of Gansu Province(GSSYLXM-04) （GSSYLXM-04）
