{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T14:23:45Z","timestamp":1780410225898,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["42072314"],"award-info":[{"award-number":["42072314"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["42177147"],"award-info":[{"award-number":["42177147"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["41807271"],"award-info":[{"award-number":["41807271"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["2012M521500"],"award-info":[{"award-number":["2012M521500"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["2014T70758"],"award-info":[{"award-number":["2014T70758"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["2017YFC1501304"],"award-info":[{"award-number":["2017YFC1501304"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["KJFZ-2018-049-001"],"award-info":[{"award-number":["KJFZ-2018-049-001"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["KJFZ-2018-049"],"award-info":[{"award-number":["KJFZ-2018-049"]}]},{"name":"China Postdoctoral Science Foundation","award":["42072314"],"award-info":[{"award-number":["42072314"]}]},{"name":"China Postdoctoral Science Foundation","award":["42177147"],"award-info":[{"award-number":["42177147"]}]},{"name":"China Postdoctoral Science Foundation","award":["41807271"],"award-info":[{"award-number":["41807271"]}]},{"name":"China Postdoctoral Science Foundation","award":["2012M521500"],"award-info":[{"award-number":["2012M521500"]}]},{"name":"China Postdoctoral Science Foundation","award":["2014T70758"],"award-info":[{"award-number":["2014T70758"]}]},{"name":"China Postdoctoral Science Foundation","award":["2017YFC1501304"],"award-info":[{"award-number":["2017YFC1501304"]}]},{"name":"China Postdoctoral Science Foundation","award":["KJFZ-2018-049-001"],"award-info":[{"award-number":["KJFZ-2018-049-001"]}]},{"name":"China Postdoctoral Science Foundation","award":["KJFZ-2018-049"],"award-info":[{"award-number":["KJFZ-2018-049"]}]},{"name":"National Key R&amp;D Program of China","award":["42072314"],"award-info":[{"award-number":["42072314"]}]},{"name":"National Key R&amp;D Program of China","award":["42177147"],"award-info":[{"award-number":["42177147"]}]},{"name":"National Key R&amp;D Program of China","award":["41807271"],"award-info":[{"award-number":["41807271"]}]},{"name":"National Key R&amp;D Program of China","award":["2012M521500"],"award-info":[{"award-number":["2012M521500"]}]},{"name":"National Key R&amp;D Program of China","award":["2014T70758"],"award-info":[{"award-number":["2014T70758"]}]},{"name":"National Key R&amp;D Program of China","award":["2017YFC1501304"],"award-info":[{"award-number":["2017YFC1501304"]}]},{"name":"National Key R&amp;D Program of China","award":["KJFZ-2018-049-001"],"award-info":[{"award-number":["KJFZ-2018-049-001"]}]},{"name":"National Key R&amp;D Program of China","award":["KJFZ-2018-049"],"award-info":[{"award-number":["KJFZ-2018-049"]}]},{"name":"China Communications Construction Company Second Highway Consultant Co., Ltd.","award":["42072314"],"award-info":[{"award-number":["42072314"]}]},{"name":"China Communications Construction Company Second Highway Consultant Co., Ltd.","award":["42177147"],"award-info":[{"award-number":["42177147"]}]},{"name":"China Communications Construction Company Second Highway Consultant Co., Ltd.","award":["41807271"],"award-info":[{"award-number":["41807271"]}]},{"name":"China Communications Construction Company Second Highway Consultant Co., Ltd.","award":["2012M521500"],"award-info":[{"award-number":["2012M521500"]}]},{"name":"China Communications Construction Company Second Highway Consultant Co., Ltd.","award":["2014T70758"],"award-info":[{"award-number":["2014T70758"]}]},{"name":"China Communications Construction Company Second Highway Consultant Co., Ltd.","award":["2017YFC1501304"],"award-info":[{"award-number":["2017YFC1501304"]}]},{"name":"China Communications Construction Company Second Highway Consultant Co., Ltd.","award":["KJFZ-2018-049-001"],"award-info":[{"award-number":["KJFZ-2018-049-001"]}]},{"name":"China Communications Construction Company Second Highway Consultant Co., Ltd.","award":["KJFZ-2018-049"],"award-info":[{"award-number":["KJFZ-2018-049"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Improving the temperature prediction accuracy for subgrades in seasonally frozen regions will greatly help improve the understanding of subgrades\u2019 thermal states. Due to the nonlinearity and non-stationarity of the temperature time series of subgrades, it is difficult for a single general neural network to accurately capture these two characteristics. Many hybrid models have been proposed to more accurately forecast the temperature time series. Among these hybrid models, the CEEMDAN-LSTM model is promising, thanks to the advantages of the long short-term memory (LSTM) artificial neural network, which is good at handling complex time series data, and its combination with the broad applicability of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) in the field of signal decomposition. In this study, by performing empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and CEEMDAN on temperature time series, respectively, a hybrid dataset is formed with the corresponding time series of volumetric water content and frost heave, and finally, the CEEMDAN-LSTM model is created for prediction purposes. The results of the performance comparisons between multiple models show that the CEEMDAN-LSTM model has the best prediction performance compared to other decomposed LSTM models because the composition of the hybrid dataset improves predictive ability, and thus, it can better handle the nonlinearity and non-stationarity of the temperature time series data.<\/jats:p>","DOI":"10.3390\/s22155742","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T23:49:27Z","timestamp":1659397767000},"page":"5742","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Temperature Prediction of Seasonal Frozen Subgrades Based on CEEMDAN-LSTM Hybrid Model"],"prefix":"10.3390","volume":"22","author":[{"given":"Liyue","family":"Chen","sequence":"first","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"},{"name":"China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0919-4820","authenticated-orcid":false,"given":"Xiao","family":"Liu","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Zeng","sequence":"additional","affiliation":[{"name":"China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianzhi","family":"He","sequence":"additional","affiliation":[{"name":"China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengguang","family":"Chen","sequence":"additional","affiliation":[{"name":"China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoshan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"key":"ref_1","unstructured":"Xu, X.Z., Wang, J.C., and Zhang, L.X. (2001). Physics of Frozen Soils, Science Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.coldregions.2012.12.001","article-title":"Present situation and prospect of mechanical research on frozen soils in China","volume":"87","author":"Lai","year":"2013","journal-title":"Cold Reg. Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Deng, Q., Liu, X., Zeng, C., He, X., Chen, F., and Zhang, S. (2021). A Freezing-Thawing Damage Characterization Method for Highway Subgrade in Seasonally Frozen Regions Based on Thermal-Hydraulic-Mechanical Coupling Model. Sensors, 21.","DOI":"10.3390\/s21186251"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, D., Chen, L., and Ming, F. (2020). Study on the Mechanical Criterion of Ice Lens Formation Based on Pore Size Distribution. Appl. Sci., 10.","DOI":"10.3390\/app10248981"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.coldregions.2019.03.019","article-title":"Binary medium creep constitutive model for frozen soils based on homogenization theory","volume":"162","author":"Wang","year":"2019","journal-title":"Cold Reg. Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Boyd, G., Na, D., Li, Z., Snowling, S., Zhang, Q., and Zhou, P. (2019). Influent Forecasting for Wastewater Treatment Plants in North America. Sustainability, 11.","DOI":"10.3390\/su11061764"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1016\/j.jempfin.2015.08.010","article-title":"Strict stationarity, persistence and volatility forecasting in ARCH (\u221e) processes","volume":"38","author":"Davidson","year":"2016","journal-title":"J. Empir. Financ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"621","DOI":"10.3390\/en5030621","article-title":"A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations","volume":"5","author":"Trombe","year":"2012","journal-title":"Energies"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xin, J., Zhou, J., Yang, S.X., Li, X., and Wang, Y. (2018). Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model. Sensors, 18.","DOI":"10.3390\/s18010298"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.3390\/s7091766","article-title":"Time series forecasting for energy-efficient organization of wireless sensor networks","volume":"7","author":"Wang","year":"2007","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yu, R., Yang, Y., Yang, L., Han, G., and Move, O.A. (2016). RAQ-A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems. Sensors, 16.","DOI":"10.3390\/s16010086"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/j.renene.2003.11.009","article-title":"Support vector machines for wind speed prediction","volume":"29","author":"Mohandes","year":"2004","journal-title":"Renew. Energy"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, J., Dai, B., Li, X., Xu, X., and Liu, D. (2019). A Dynamic Bayesian Network for Vehicle Maneuver Prediction in Highway Driving Scenarios: Framework and Verification. Electronics, 8.","DOI":"10.3390\/electronics8010040"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.apenergy.2014.05.055","article-title":"Artificial neural network based daily local forecasting for global solar radiation","volume":"130","author":"Amrouche","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fan, J., Liu, C., Lv, Y., Han, J., and Wang, J. (2019). A Short-Term Forecast Model of foF2 Based on Elman Neural Network. Appl. Sci., 9.","DOI":"10.3390\/app9142782"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.eswa.2017.04.013","article-title":"Random forests-based extreme learning machine ensemble for multi-regime time series prediction","volume":"83","author":"Lin","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1016\/j.renene.2015.07.004","article-title":"Wind speed forecasting for wind farms: A method based on support vector regression","volume":"85","author":"Gershenson","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.ins.2014.12.031","article-title":"A weighted LS-SVM based learning system for time series forecasting","volume":"299","author":"Chen","year":"2015","journal-title":"Inform. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.jhazmat.2013.10.052","article-title":"A robust framework to predict mercury speciation in combustion flue gases","volume":"264","author":"Ticknor","year":"2014","journal-title":"J. Hazard. Mater."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Khellal, A., Ma, H., and Fei, Q. (2018). Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images. Sensors, 18.","DOI":"10.3390\/s18051490"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3234","DOI":"10.1016\/j.eswa.2014.12.003","article-title":"Recurrent neural network and a hybrid model for prediction of stock returns","volume":"42","author":"Rather","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104785","DOI":"10.1016\/j.knosys.2019.05.028","article-title":"EA-LSTM: Evolutionary attention-based LSTM for time series prediction","volume":"181","author":"Li","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.patcog.2016.10.016","article-title":"Accurate recognition of words in scenes without character segmentation using recurrent neural network","volume":"63","author":"Su","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1016\/j.csl.2014.01.001","article-title":"Feature enhancement by deep LSTM networks for ASR in reverberant multisource environments","volume":"28","author":"Weninger","year":"2014","journal-title":"Comput. Speech Lang."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.jhydrol.2018.04.065","article-title":"Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas","volume":"561","author":"Zhang","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","article-title":"Long short-term memory neural network for traffic speed prediction using remote microwave sensor data","volume":"54","author":"Ma","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xue, H., Huynh, D.Q., and Reynolds, M. (2018, January 12\u201315). SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00135"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.neucom.2018.04.045","article-title":"LSTM with sentence representations for document-level sentiment classification","volume":"308","author":"Rao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. London. Ser. A Math. Phys. Eng. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemble empirical mode decomposition: A noise-assisted data analysis method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Torres, M.E., Colominas, M.A., Schlotthauer, G., and Flandrin, P. (2011, January 22\u201327). A complete ensemble empirical mode decomposition with adaptive noise. Proceedings of the 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947265"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sha, J., Li, X., Zhang, M., and Wang, Z.-L. (2021). Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks. Water, 13.","DOI":"10.3390\/w13111547"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, Q., Zhang, G., Nie, Z., and Gui, Z. (2018). A Hybrid Model for Annual Runoff Time Series Forecasting Using Elman Neural Network with Ensemble Empirical Mode Decomposition. Water, 10.","DOI":"10.3390\/w10040416"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zheng, H., Yuan, J., and Chen, L. (2017). Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation. Energies, 10.","DOI":"10.3390\/en10081168"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lei, Z., and Su, W. (2019). Mold Level Predict of Continuous Casting Using Hybrid EMD-SVR-GA Algorithm. Processes, 7.","DOI":"10.3390\/pr7030177"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, Q., Zhang, G., Nie, Z., Gui, Z., and Que, H. (2018). A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15051032"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jiang, X., Wei, P., Luo, Y., and Li, Y. (2021). Air Pollutant Concentration Prediction Based on a CEEMDAN-FE-BiLSTM Model. Atmosphere, 12.","DOI":"10.3390\/atmos12111452"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lin, H., and Sun, Q. (2020). Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks. Energies, 13.","DOI":"10.3390\/en13071543"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Wang, L., and Qian, J. (2022). Application of Combined Models Based on Empirical Mode Decomposition, Deep Learning, and Autoregressive Integrated Moving Average Model for Short-Term Heating Load Predictions. Sustainability, 14.","DOI":"10.3390\/su14127349"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lin, H., Sun, Q., and Chen, S.-Q. (2020). Reducing Exchange Rate Risks in International Trade: A Hybrid Forecasting Approach of CEEMDAN and Multilayer LSTM. Sustainability, 12.","DOI":"10.3390\/su12062451"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"113315","DOI":"10.1016\/j.apenergy.2019.113315","article-title":"A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network","volume":"251","author":"Wang","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4296","DOI":"10.1007\/s10489-020-01814-0","article-title":"A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network","volume":"50","author":"Niu","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_45","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_46","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5742\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:41Z","timestamp":1760140841000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5742"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,1]]},"references-count":46,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155742"],"URL":"https:\/\/doi.org\/10.3390\/s22155742","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,1]]}}}