以云南省天星站和坡脚站10、20、40 cm 3个土层的土壤含水量观测数据为基础,通过改进时变滤波经验模态分解(TVFEMD)和快速学习网(FLN)方法构建基于多种优化算法的预测模型(TVFEMD-BSLO/AO/IVYA/EGO/PSO-FLN),提升土壤含水量时间序列预...以云南省天星站和坡脚站10、20、40 cm 3个土层的土壤含水量观测数据为基础,通过改进时变滤波经验模态分解(TVFEMD)和快速学习网(FLN)方法构建基于多种优化算法的预测模型(TVFEMD-BSLO/AO/IVYA/EGO/PSO-FLN),提升土壤含水量时间序列预测精度。通过比较各优化算法的模型性能,为土壤水分预测提供更优的建模方法。结果表明,TVFEMD分解效果主要受带宽阈值和B样条阶数2个关键参数影响。采用IVYA算法优化这2个参数可提升时间序列分解质量,进而改善模型预测性能。TVFEMD-BLSO/AO/IVYA/EGO-FLN模型在训练集上表现出卓越的预测性能,其平均绝对百分比误差(MAPE)为0.002%~0.077%,决定系数(R^(2))为0.9997~1.0000;预测集中的MAPE为0.006%~0.459%,R^(2)为0.9966~1.0000。与TVFEMD-PSO-FLN模型相比,TVFEMD-BLSO/AO/IVYA/EGO-FLN模型在拟合性能和预测精度方面均有明显提升。采用BLSO、AO、IVYA和EGO算法优化FLN超参数可有效提升模型性能,其中IVYA算法的优化效果较突出。展开更多
This research aims to develop an advanced deep learning-based ensemble algorithm,utilizing environmental temperature and solar radiation as feature factors,to conduct hourly temperature field predictions for steel-con...This research aims to develop an advanced deep learning-based ensemble algorithm,utilizing environmental temperature and solar radiation as feature factors,to conduct hourly temperature field predictions for steel-concrete composite decks(SCCDs).The proposed model comprises feature parameter lag selection,two non-stationary time series decomposition methods(empirical mode decomposition(EMD)and time-varying filtering-based empirical mode decomposition(TVFEMD)),and a stacking ensemble prediction model.To validate the proposed model,five machine learning(ML)models(random forest(RF),support vector regression(SVR),multilayer perceptron(MLP),gradient boosting regression(GBR),and extreme gradient boosting(XGBoost))were tested as base learners and evaluations were conducted within independent,mixed,and ensemble frameworks.Finally,predictions are made based on engineering cases.The results indicate that consideration of lag variables and modal decomposition can significantly improve the prediction performance of learners,and the stacking framework,which combines multiple learners,achieves superior prediction results.The proposed method demonstrates a high degree of predictive robustness and can be applied to statistical analysis of the temperature field in SCCDs.Incorporating time lag features helps account for the delayed heat dissipation phenomenon in concrete,while decomposition techniques assist in feature extraction.展开更多
文摘以云南省天星站和坡脚站10、20、40 cm 3个土层的土壤含水量观测数据为基础,通过改进时变滤波经验模态分解(TVFEMD)和快速学习网(FLN)方法构建基于多种优化算法的预测模型(TVFEMD-BSLO/AO/IVYA/EGO/PSO-FLN),提升土壤含水量时间序列预测精度。通过比较各优化算法的模型性能,为土壤水分预测提供更优的建模方法。结果表明,TVFEMD分解效果主要受带宽阈值和B样条阶数2个关键参数影响。采用IVYA算法优化这2个参数可提升时间序列分解质量,进而改善模型预测性能。TVFEMD-BLSO/AO/IVYA/EGO-FLN模型在训练集上表现出卓越的预测性能,其平均绝对百分比误差(MAPE)为0.002%~0.077%,决定系数(R^(2))为0.9997~1.0000;预测集中的MAPE为0.006%~0.459%,R^(2)为0.9966~1.0000。与TVFEMD-PSO-FLN模型相比,TVFEMD-BLSO/AO/IVYA/EGO-FLN模型在拟合性能和预测精度方面均有明显提升。采用BLSO、AO、IVYA和EGO算法优化FLN超参数可有效提升模型性能,其中IVYA算法的优化效果较突出。
基金National Natural Science Foundation of China(No.52278235)Science and Technology Program of Hunan Provincial Department of Transportation(No.202309),China.
文摘This research aims to develop an advanced deep learning-based ensemble algorithm,utilizing environmental temperature and solar radiation as feature factors,to conduct hourly temperature field predictions for steel-concrete composite decks(SCCDs).The proposed model comprises feature parameter lag selection,two non-stationary time series decomposition methods(empirical mode decomposition(EMD)and time-varying filtering-based empirical mode decomposition(TVFEMD)),and a stacking ensemble prediction model.To validate the proposed model,five machine learning(ML)models(random forest(RF),support vector regression(SVR),multilayer perceptron(MLP),gradient boosting regression(GBR),and extreme gradient boosting(XGBoost))were tested as base learners and evaluations were conducted within independent,mixed,and ensemble frameworks.Finally,predictions are made based on engineering cases.The results indicate that consideration of lag variables and modal decomposition can significantly improve the prediction performance of learners,and the stacking framework,which combines multiple learners,achieves superior prediction results.The proposed method demonstrates a high degree of predictive robustness and can be applied to statistical analysis of the temperature field in SCCDs.Incorporating time lag features helps account for the delayed heat dissipation phenomenon in concrete,while decomposition techniques assist in feature extraction.
