华南理工大学学报（自然科学版）2026，Vol.54Issue(5)：59-76,18.DOI:10.12141/j.issn.1000-565X.250240
基于混合特征选择与IVMD-AOO-BiLSTM的交通运输业碳排放预测
Carbon Emission Prediction in Transportation Industry Based on Hybrid Feature Selection and an IVMD-AOO-BiLSTM
摘要
Abstract
To address the nonlinear and volatile characteristics of carbon emission data sequences in the transportation industry,as well as the low prediction accuracy caused by the coupling of multiple influencing factors,this study develops a carbon emission prediction model that combines hybrid feature engineering(RF-MIC),improved variational mode decomposition(IVMD),the animated oat optimization algorithm(AOO),and bidirectional long short-term memory(Bi-LSTM).First,a hybrid feature selection method Based on random forest(RF)and the maximal information coefficient(MIC)is constructed to quantify the contribution of each factor,remove redundant disturbances,and identify the key drivers of carbon emissions.Second,a multi-objective decomposition framework based on variational mode decomposition(VMD)is constructed by using the escape optimization algorithm(ESC)and Pareto optimality to adaptively optimize the number of modes K and the penalty factor α.The original carbon emission sequence is then decomposed into a series of stationary modal components,thereby mitigating its nonlinearity and volatility.Third,an AOO-based BiLSTM hyperparameter optimization theory is established,where AOO is employed to globally optimize hyperparameters such as the number of hidden layer neurons and the learning rate of BiLSTM,preventing the model from falling into local optima.Finally,prediction sub-models based on AOO-BiLSTM are constructed for each modal component,and the predicted results of all components are integrated and reconstructed to obtain the final prediction value.The proposed model is validated using carbon emission data from China's transportation industry from 1990 to 2023.The results show that,compared with the optimal benchmark model,the root mean square error(RMSE),mean absolute error(MAE),and mean absolute percentage error(MAPE)of the proposed model are reduced by 35.77%,40.48%,and 59.52%,respectively,demonstrating its effectiveness in predicting carbon emissions in the transportation industry.关键词
特征选择/逃生优化算法/帕累托最优解/变分模态分解/长颖燕麦优化算法/碳排放预测Key words
feature selection/escape optimization algorithm/Pareto optimal solution/variational mode decomposition/animated oat optimization algorithm/carbon emission prediction分类
信息技术与安全科学引用本文复制引用
王庆荣,张金鹏,朱昌锋,余娴妹..基于混合特征选择与IVMD-AOO-BiLSTM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）,2026,54(5):59-76,18.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"Major Research Programs of the Educational Department of Gansu Province(GSSYLXM-04) （GSSYLXM-04）
