Journal of South China University of Technology(Natural Science) >
Carbon Emission Prediction in Transportation Industry Based on SD-ISSA-DALSTM
Received date: 2024-07-15
Online published: 2024-11-12
Supported by
the National Natural Science Foundation of China(72161024);the “Double-First Class” Major Research Programs of the Educational Department of Gansu Province(GSSYLXM-04)
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.
WANG Qingrong , WANG Junjie , ZHU Changfeng , HAO Fule . Carbon Emission Prediction in Transportation Industry Based on SD-ISSA-DALSTM[J]. Journal of South China University of Technology(Natural Science), 2025 , 53(5) : 66 -81 . DOI: 10.12141/j.issn.1000-565X.240356
| 1 | GAO M Y， YANG H L， XIAO Q Z，et al ．A novel method for carbon emission forecasting based on Gom-pertz’s law and fractional grey model：evidence from American industrial sector［J］．Renewable Energy，2022，181：803-819． |
| 2 | 朱长征，杨莎，刘鹏博，等 ．中国交通运输业碳达峰时间预测研究［J］．交通运输系统工程与信息，2022，22（6）：291-299． |
| ZHU Chang-zheng， YANG Sha， LIU Peng-bo，et al ．Carbon dioxide emission peak study of transportation industry in China［J］．Journal of Transportation Systems Engineering and Information Technology，2022，22（6）：291-299． | |
| 3 | 赵金辉，李景顺，王潘乐，等 ．基于Lasso-BP神经网络模型的河南省碳达峰路径研究［J］．环境工程，2022，40（12）：151-156，164． |
| ZHAO Jinhui， LI Jingshun， WANG Panle，et al ．A study of carbon peak paths in Henan province based on Lasso regression-BP neural network model［J］．Environmental Engineering，2022，40（12）：151-156，164． | |
| 4 | YANG H J， O’CONNELL J F ．Short-term carbon emissions forecast for aviation industry in Shanghai［J］．Journal of Cleaner Production，2020，275：122734/1-12． |
| 5 | 胡茂峰，郑义彬，李宇涵 ．多情景下湖北省交通运输碳排放峰值预测研究［J］．环境科学学报，2022，42（4）：464-472． |
| HU Maofeng， ZHENG Yibin， LI Yuhan ．Forecasting of transport carbon emission peak in Hubei province under multiple scenarios［J］．Acta Scientiae Circumstantiae，2022，42（4）：464-472． | |
| 6 | 刘慧甜，胡大伟 ．基于机器学习的交通碳排放预测模型构建与分析［J］．环境科学，2024，45（6）：3421-3432． |
| LIU Hui-tian， HU Da-wei ．Construction and analysis of machine learning based transportation carbon emission prediction model［J］．Environmental Science，2024，45（6）：3421-3432． | |
| 7 | 曾弘锐，孙文昊，何卫，等 ．基于机器学习的铁路隧道施工碳排放预测模型研究［J］．现代隧道技术，2023，60（6）：29-39． |
| ZENG Hongrui， SUN Wenhao， HE Wei，et al ．Study on the carbon emission prediction model for railway tunnel construction based on machine learning［J］．Modern Tunnelling Technology，2023，60（6）：29-39． | |
| 8 | 陈亮，王金泓，何涛，等 ．基于SVR的区域交通碳排放预测研究［J］．交通运输系统工程与信息，2018，18（2）：13-19． |
| CHEN Liang， WANG Jin-hong， HE Tao，et al ．Forecast study of regional transportation carbon emissions based on SVR［J］．Journal of Transportation Systems Engineering and Information Technology，2018，18（2）：14-19． | |
| 9 | 夏晓圣，陈菁菁，王佳佳，等 ．基于随机森林模型的中国PM2.5浓度影响因素分析［J］．环境科学，2020，41（5）：2057-2065． |
| XIA Xiao-sheng， CHEN Jing-jing， WANG Jia-jia，et al ．PM2.5 concentration influencing factors in China based on the random forest model［J］．Environmental Science，2020，41（5）：2057-2065． | |
| 10 | 刘淳森，曲建升，葛钰洁，等 ．基于LSTM模型的中国交通运输业碳排放预测［J］．中国环境科学，2023，43（5）：2574-2582． |
| LIU Chun-sen， QU Jian-sheng， GE Yu-jie，et al ．Carbon emission forecasting in China’s transportation sector based on LSTM model［J］．China Environmental Science，2023，43（5）：2574-2582． | |
| 11 | 连艳琼，苏墩煌，施生旭 ．基于STIRPAT和CNN-LSTM组合模型的福建省碳达峰预测［J］．环境科学，2025，46（1）：10-18． |
| LIAN Yan-qiong， SU Dun-huang， SHI Sheng-xu ．Carbon peak prediction in Fujian province based on combined STIRPAT and CNN-LSTM models［J］．Environmental Science，2025，46（1）：10-18． | |
| 12 | 高金武，贾志桓，王向阳，等 ．基于PSO-LSTM的质子交换膜燃料电池退化趋势预测［J］．吉林大学学报（工学版），2022，52（9）：2192-2202． |
| GAO Jin-wu， JIA Zhi-heng， WANG Xiang-yang，et al ．Degradation trend prediction of proton exchange membrane fuel cell based on PSO-LSTM［J］．Journal of Jilin University （Engineering and Technology Edition），2022，52（9）：2192-2202． | |
| 13 | 汪恩良，田雨，刘兴超，等 ．基于WOA-BP神经网络的超低温冻土抗压强度预测模型研究［J］．力学学报，2022，54（04）：1145-1153． |
| WANG Enliang， TIAN Yu， LIU Xingchao，et al ．Predition model of compressive strength of ultra low temperature frozen soil based on WOA-BP neural network［J］．Chinese Journal of Theoretical and Applied Mechanics，2022，54（4）：1145-1153． | |
| 14 | 祖林禄，柳平增，赵妍平，等 ．基于SSA-LSTM的日光温室环境预测模型研究［J］．农业机械学报，2023，54（2）：351-358． |
| ZU Linlu， LIU Pingzeng， ZHAO Yanping，et al ．Solar greenhouse environment prediction model baesd on SSA-LSTM［J］．Transactions of the Chinese Society for Agricultural Machinery，2023，54（2）：351-358． | |
| 15 | 王庆荣，王俊杰，朱昌锋，等 ．融合VMD和SSA-LSSVM的交通运输业碳排放预测研究［J］．环境工程，2023，41（10）：124-132． |
| WANG Qingrong， WANG Junjie， ZHU Changfeng，et al ．Carbon emission prediction of transportation industry based on VMD and SSA-LSSVM［J］．Environmental Engineering，2023，41（10）：124-132． | |
| 16 | 张新生，任明月，陈章政 ．基于CEEMD-SSA-ELM方法的建筑业碳排放预测研究［J］．生态经济，2023，39（10）：33-39，88． |
| ZHANG Xinsheng， REN Mingyue， CHEN Zhangzheng ．Research on carbon emission prediction of construction industry based on CEEMD-SSA-ELM method［J］．Ecological Economy，2023，39（10）：33-39，88． | |
| 17 | 李爱莲，全凌翔，崔桂梅，等 ．融合正余弦和柯西变异的麻雀搜索算法［J］．计算机工程与应用，2022，58（3）：91-99． |
| LI Ailian，QUAN Lingxiang，CUI Guimei，et al，Sparrow search algorithm combining sine-cosine and Cauchy mutation［J］．Computer Engineering and Applications，2022，58（3）：91-99． | |
| 18 | 回立川，陈雪莲，孟嗣博 ．多策略混合的改进麻雀搜索算法［J］．计算机工程与应用，2022，58（16）：71-83． |
| HUI Lichuan， CHEN Xuelian， MENG Sibo，et al ．Improved sparrow search algorithm based on multi-strategy mixing［J］．Computer Engineering and Applications，2022，58（16）：71-83． | |
| 19 | 张雯，吴志彬，徐玖平 ．基于EMD-PSO-LSSVM的碳排分解集成预测方法［J］．控制与决策，2022，37（7）：1837-1846． |
| ZHANG Wen， WU Zhi-bin， XU Jiu-ping ．A decomposition-integration forecasting method of carbon emission based on EMD-PSO-LSSVM［J］．Control and Decision，2022，37（7）：1837-1846． | |
| 20 | SUN W， REN C ．Short-term prediction of carbon emissions based on the EEMD-PSO-BP model［J］．Environmental Science and Pollution Research，2021，28（40）：56580-56594． |
| 21 | SUN W， HUANG C ．A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network［J］．Journal of Cleaner Production，2020，243：118671/1-13． |
| 22 | FENG K， SONG J， YANG Z ．A daily carbon emission prediction model combining two-stage feature selection and optimized extreme learning machine［J］．Environmental Science and Pollution Research，2022，29：64983-64998． |
| 23 | 武新章，梁祥宇，朱虹谕，等 ．基于CEEMDAN-GRA-PCC-ATCN的短期风电功率预测［J］．山东大学学报（工学版），2022，52（6）：146-156． |
| WU Xinzhang， LIANG Xiangyu， ZHU Hongyu，et al ．Short-term wind power prediction based on CEEMDAN-GRA-PCC-ATCN［J］．Journal of Shandong University （Engineering Science），2022，52（6）：146-156． | |
| 24 | ZHANG H， KONG X， REN C ．Influencing factors and forecast of carbon emissions from transportation-taking Shandong province as an example［J］．IOP Conference Series： Earth and Environmental Science，2019，300（3）：032063/1-7． |
| 25 | QUAN C， CHENG X， YU S，et al ．Analysis on the influencing factors of carbon emission in China’s logistics industry based on LMDI method［J］．The Science of the Total Environment，2020，2020（734）：138473-138473． |
| 26 | 曾晓莹，邱荣祖，林丹婷，等 ．中国交通碳排放及影响因素时空异质性［J］．中国环境科学，2020，40（10）：4304-4313． |
| ZENG Xiao-ying， QIU Rong-zu， LIN Dan-ting，et al ．Spatio-temporal heterogeneity of transportation carbon emissions and its influencing factors in China［J］．China Environmental Science，2020，40（10）：4304-4313． | |
| 27 | 高金贺，黄伟玲，蒋浩鹏 ．城市交通碳排放预测的多模型对比分析［J］．重庆交通大学学报（自然科学版），2020，39（7）：33-39． |
| GAO Jinhe， HHUANG Weiling， JIANG Haopeng ．Comparison of multiple forecast models of urban traffic carbon emissions［J］．Journal of Chongqing Jiaotong University （Natural Sciences），2020，39（7）：33-39． | |
| 28 | IPCC ．2006 IPCC guidelines for national greenhouse gas inventories［R］．Cambridge：Cambridge University Press，2006． |
| 29 | 王瑞，张璐婷，逯静 ．基于新型相似日选取和VMD-NGO-BiGRU的短期光伏功率预测［J］．湖南大学学报（自然科学版），2024，51（2）：68-80． |
| WANG Rui， ZHANG Luting， LU Jing ．Short term photovoltaic power prediction based on new similar day selection and VMD-NGO-BiGRU［J］．Journal of Hunan University （Natural Sciences），2024，51（2）：68-80． |
/
| 〈 |
|
〉 |