{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T21:39:06Z","timestamp":1773956346885,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T00:00:00Z","timestamp":1644364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of Shandong Provincial, China","award":["ZR202102180570"],"award-info":[{"award-number":["ZR202102180570"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>It is crucial to predict landslide displacement accurately for establishing a reliable early warning system. Such a requirement is more urgent for landslides in the reservoir area. The main reason is that an inaccurate prediction can lead to riverine disasters and secondary surge disasters. Machine learning (ML) methods have been developed and commonly applied in landslide displacement prediction because of their powerful nonlinear processing ability. Recently, deep ML methods have become popular, as they can deal with more complicated problems than conventional ML methods. However, it is usually not easy to obtain a well-trained deep ML model, as many hyperparameters need to be trained. In this paper, a deep ML method\u2014the gated recurrent unit (GRU)\u2014with the advantages of a powerful prediction ability and fewer hyperparameters, was applied to forecast landslide displacement in the dam reservoir. The accumulated displacement was firstly decomposed into a trend term, a periodic term, and a stochastic term by complementary ensemble empirical mode decomposition (CEEMD). A univariate GRU model and a multivariable GRU model were employed to forecast trend and stochastic displacements, respectively. A multivariable GRU model was applied to predict periodic displacement, and another two popular ML methods\u2014long short-term memory neural networks (LSTM) and random forest (RF)\u2014were used for comparison. Precipitation, reservoir level, and previous displacement were considered to be candidate-triggering factors for inputs of the models. The Baijiabao landslide, located in the Three Gorges Reservoir Area (TGRA), was taken as a case study to test the prediction ability of the model. The results demonstrated that the GRU algorithm provided the most encouraging results. Such a satisfactory prediction accuracy of the GRU algorithm depends on its ability to fully use the historical information while having fewer hyperparameters to train. It is concluded that the proposed model can be a valuable tool for predicting the displacements of landslides in the TGRA and other dam reservoirs.<\/jats:p>","DOI":"10.3390\/s22041320","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T21:26:48Z","timestamp":1644442008000},"page":"1320","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Using Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit to Predict Landslide Displacements in Dam Reservoir"],"prefix":"10.3390","volume":"22","author":[{"given":"Beibei","family":"Yang","sequence":"first","affiliation":[{"name":"School of Civil Engineering, Yantai University, Yantai 264005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2845-7577","authenticated-orcid":false,"given":"Ting","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Luqi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chongqing University, Chongqing 400044, China"}]},{"given":"Wei","family":"Huang","sequence":"additional","affiliation":[{"name":"The Seventh Geological Brigade of Hubei Geological Bureau, Yichang 443000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1007\/s10346-020-01424-4","article-title":"Landslides across the USA: Occurrence, susceptibility, and data limitations","volume":"17","author":"Mirus","year":"2020","journal-title":"Landslides"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.geomorph.2014.02.032","article-title":"Landslide incidence in the North of Portugal analysis of a historical landslide database based on press releases and technical reports","volume":"214","author":"Pereira","year":"2014","journal-title":"Geomorphology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105350","DOI":"10.1016\/j.enggeo.2019.105350","article-title":"Energy evolution: A new perspective on the failure mechanism of purplish-red mudstones from the Three Gorges Reservoir area, China","volume":"264","author":"Wen","year":"2020","journal-title":"Eng. Geol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Roodposhti, M.S., Aryal, J., and Pradhan, B. (2019). A novel rule-based approach in mapping landslide susceptibility. Sensors, 19.","DOI":"10.3390\/s19102274"},{"key":"ref_5","unstructured":"Yang, B.B. (2019). Deformation Characteristics and Displacement Prediction of Colluvial Landslides in Wanzhou County, Three Georges Reservoir. [Ph.D. Thesis, China University of Geosciences]."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xiao, T., Yu, L., Tian, W., Zhou, C., and Wang, L. (2021). Reducing local correlations among causal factor classifications as a strategy to improve landslide susceptibility mapping. Front. Earth Sci., 997.","DOI":"10.3389\/feart.2021.781674"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1007\/s12665-017-6898-9","article-title":"Annual variation of landslide stability under the effect of water level fluctuation and rainfall in the Three Gorges Reservoir, China","volume":"76","author":"Yang","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1007\/s10346-019-01299-0","article-title":"A step beyond landslide susceptibility maps: A simple method to investigate and explain the different outcomes obtained by different approaches","volume":"17","author":"Xiao","year":"2020","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.5194\/nhess-17-2181-2017","article-title":"Landslide displacement prediction using the GA-LSSVM model and time series analysis: A case study of Three Gorges Reservoir, China","volume":"17","author":"Wen","year":"2017","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s10346-018-01127-x","article-title":"Time series analysis and long short-term memory neural network to predict landslide displacement","volume":"16","author":"Yang","year":"2019","journal-title":"Landslides"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104527","DOI":"10.1016\/j.cageo.2020.104527","article-title":"A hybrid prediction model of landslide displacement with risk-averse adaptation","volume":"141","author":"Xing","year":"2020","journal-title":"Comput. Geosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1007\/s10346-018-1057-2","article-title":"Numerical modeling of the June 24, 2015, Hongyanzi Landslide generated impulse waves in Three Gorges Reservoir, China","volume":"15","author":"Xiao","year":"2018","journal-title":"Landslides"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.enggeo.2014.11.008","article-title":"Training enhanced reservoir computing predictor for landslide displacement","volume":"188","author":"Yao","year":"2015","journal-title":"Eng. Geol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8466","DOI":"10.1007\/s10489-021-02337-y","article-title":"Prediction interval estimation of landslide displacement using adaptive chicken swarm optimization-tuned support vector machines","volume":"51","author":"Xing","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1016\/j.optlaseng.2011.01.010","article-title":"A novel distributed optic fiber transduser for landslides monitoring","volume":"49","author":"Zhu","year":"2011","journal-title":"Opt. Lasers Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"185","DOI":"10.5194\/nhess-7-185-2007","article-title":"Validation of landslide hazard assessment by means of GPS monitoring technique-a case study in the Dolomites (Eastern Alps, Italy)","volume":"7","author":"Tagliavini","year":"2007","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s10346-011-0277-5","article-title":"Kinematics of the Cerca del Cielo, Puerto Rico landslide derived from GPS observations","volume":"9","author":"Wang","year":"2012","journal-title":"Landslides"},{"key":"ref_18","unstructured":"Saito, M. (1965, January 8\u201315). Forecasting the time of occurrence of a slope failure. Proceedings of the 6th International Mechanics and Foundation Engineering, Montreal, QC, Canada."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/s12665-015-5022-2","article-title":"Application of a two-step cluster analysis and the Apriori algorithm to classify the deformation states of two typical colluvial landslides in the Three Gorges, China","volume":"75","author":"Wu","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.cageo.2012.08.023","article-title":"A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS","volume":"51","author":"Pradhan","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/S0167-6911(82)80025-X","article-title":"Control problems of grey systems","volume":"1","author":"Deng","year":"1982","journal-title":"Syst. Control Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.enggeo.2007.01.013","article-title":"Regression models for estimating coseismic landslide displacement","volume":"91","author":"Jibson","year":"2007","journal-title":"Eng. Geol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s10346-012-0326-8","article-title":"Displacement prediction in colluvial landslides, Three Gorges Reservoir, China","volume":"10","author":"Du","year":"2013","journal-title":"Landslides"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1061\/(ASCE)1532-3641(2002)2:2(153)","article-title":"Neural networks for slope movement prediction","volume":"2","author":"Mayoraz","year":"2002","journal-title":"Int. J. Geomech."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1007\/s10346-015-0596-z","article-title":"Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors","volume":"13","author":"Cao","year":"2016","journal-title":"Landslides"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1007\/s11069-021-04655-3","article-title":"Research on displacement prediction of step-type landslide under the influence of various environmental factors based on intelligent WCA-ELM in the Three Gorges Reservoir area","volume":"107","author":"Zhang","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7287","DOI":"10.1038\/s41598-018-25567-6","article-title":"A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms","volume":"8","author":"Zhou","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1007\/s10346-018-1020-2","article-title":"Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models","volume":"15","author":"Li","year":"2018","journal-title":"Landslides"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1080\/19475705.2021.1891145","article-title":"Modelling and predicting landslide displacements and uncertainties by multiple machine-learning algorithms: Application to Baishuihe landslide in Three Gorges Reservoir, China","volume":"12","author":"Jiang","year":"2021","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105975","DOI":"10.1016\/j.enggeo.2020.105975","article-title":"Landslide displacement prediction using kinematics-based random forests method: A case study in Jinping Reservoir area, China","volume":"283","author":"Hu","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1631\/jzus.A2000005","article-title":"Algorithms for intelligent prediction of landslide displacements","volume":"21","author":"Liu","year":"2020","journal-title":"J. Zhejiang Univ.\u2014SCIENCE A"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.enggeo.2016.02.009","article-title":"Application of time series analysis and PSO-SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China","volume":"204","author":"Zhou","year":"2016","journal-title":"Eng. Geol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1007\/s10346-017-0883-y","article-title":"Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model","volume":"15","author":"Miao","year":"2017","journal-title":"Landslides"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bui, D., Bui, K., Bui, Q., Doan, C., and Hoang, N. (2017). Model based on least squares support vector regression and artificial bee colony optimization for time-series modeling and forecasting horizontal displacement of hydropower dam. Handb. Neural Comput., 279\u2013293.","DOI":"10.1016\/B978-0-12-811318-9.00015-6"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0169-555X(01)00122-2","article-title":"A nonlinear dynamical model of landslide evolution","volume":"43","author":"Qin","year":"2002","journal-title":"Geomorphology"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cageo.2017.10.013","article-title":"Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short term memory neural network in Three Gorges area, China","volume":"111","author":"Xu","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jiang, H., Li, Y., Zhou, C., Hong, H., Glade, T., and Yin, K. (2020). Landslide displacement prediction combining LSTM and SVR algorithms: A case study of Shengjibao Landslide from the Three Gorges Reservoir Area. Appl. Sci., 10.","DOI":"10.3390\/app10217830"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, K., Gholamnia, K., Meena, S., Tiede, D., and Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.gsf.2020.02.012","article-title":"Landslide identification using machine learning","volume":"12","author":"Wang","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1007\/s10346-019-01314-4","article-title":"Landslide displacement prediction based on variational mode decomposition and WA-GWO-BP model","volume":"17","author":"Guo","year":"2019","journal-title":"Landslides"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Niu, X., Ma, J., Wang, Y., Zhang, J., Chen, H., and Tang, H. (2021). A novel decomposition-ensemble learning model based on ensemble empirical mode decomposition and recurrent neural network for landslide displacement prediction. Appl. Sci., 11.","DOI":"10.3390\/app11104684"},{"key":"ref_42","unstructured":"Yang, B., Liu, Z., Lacasse, S., and Nadim, F. (2019, January 1\u20136). Landslide displacement prediction based on wavelet transform and long short-term memory neural network. Proceedings of the XVII European Conference on Soil Mechanics and Geotechnical Engineering, Reykjavik, Iceland."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, J., Tang, H., Wen, T., Ma, J., Tan, Q., Xia, D., Xiu, X., and Zhang, Y. (2020). A hybrid landslide displacement prediction method based on CEEMD and DTW-ACO-SVR-Cases studied in the Three Gorges Reservoir Area. Sensors, 20.","DOI":"10.3390\/s20154287"},{"key":"ref_44","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."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1142\/S1793536910000422","article-title":"Complementary ensemble empirical mode decomposition a novel noise enhanced data analysis method","volume":"2","author":"Yeh","year":"2010","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","unstructured":"Gers, F.A., and Schmidhuber, J. (2000, January 27). Recurrent nets that time and count. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy.","DOI":"10.1109\/IJCNN.2000.861302"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fan, Y., Qian, Y., Xie, F.L., and Soong, F.K. (2014, January 14\u201318). TTS Synthesis with bidirectional LSTM based recurrent neural networks. Proceedings of the Fifteenth Annual Conference of the International Speech Communication QAssociation, Singapore.","DOI":"10.21437\/Interspeech.2014-443"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"14441","DOI":"10.1007\/s00521-021-06084-6","article-title":"A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data","volume":"33","author":"Ma","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rajbhandari, S., Aryal, J., Osborn, J., Musk, R., and Lucieer, A. (2017). Benchmarking the applicability of ontology in geographic object-based image analysis. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6120386"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2515","DOI":"10.1007\/s10346-020-01476-6","article-title":"A comparative study of random forests and multiple linear regression in the prediction of landslide velocity","volume":"17","author":"Gazibara","year":"2020","journal-title":"Landslides"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.atmosenv.2018.04.004","article-title":"PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors","volume":"183","author":"Zhu","year":"2018","journal-title":"Atmos. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1007\/s00477-021-02029-6","article-title":"Reliability of the prediction model for landslide displacement with step-like behavior","volume":"35","author":"Fu","year":"2021","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1080\/03036758.1988.10429158","article-title":"Landslides causes, consequences and environment","volume":"18","author":"Selby","year":"1988","journal-title":"J. R. Soc. N. Zealand"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1007\/s10706-017-0340-7","article-title":"Identifying the main control factors for different deformation stages of landslide","volume":"36","author":"Tan","year":"2018","journal-title":"Geotech. Geol. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1007\/s12665-021-09696-2","article-title":"A variable weight combination model for prediction on landslide displacement using AR model, LSTM model, and SVM model: A case study of the Xinming landslide in China","volume":"80","author":"Li","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.enggeo.2014.11.014","article-title":"Multiple neural networks switched prediction for landslide displacement","volume":"186","author":"Lian","year":"2015","journal-title":"Eng. Geol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3126\/jist.v24i2.27247","article-title":"Analysis of gradient descent optimization techniques with gated recurrent unit for stock price prediction: A case study on banking sector of Nepal stock exchange","volume":"24","author":"Saud","year":"2019","journal-title":"J. Inst. Sci. Technol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"85","DOI":"10.5194\/nhess-13-85-2013","article-title":"Brief communication \u201cLandslide Early Warning System: Toolbox and general concepts\u201d","volume":"13","author":"Intrieri","year":"2013","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.earscirev.2019.03.019","article-title":"Forecasting the time of failure of landslides at slope-scale: A literature review","volume":"193","author":"Intrieri","year":"2019","journal-title":"Earth-Sci. Rev."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1007\/s11431-011-4640-5","article-title":"Some new pre-warning criteria for creep slope failure","volume":"54","author":"Xu","year":"2011","journal-title":"Sci. China Technol. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1320\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:17:01Z","timestamp":1760134621000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1320"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,9]]},"references-count":63,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22041320"],"URL":"https:\/\/doi.org\/10.3390\/s22041320","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,9]]}}}