A data enhancement-based quadratic imputation framework for consecutive missing values considering spatiotemporal characteristics of dam deformation

Zhao EF, Wu CQ (2021) Centroid deformation-based nonlinear safety monitoring model for arch dam performance evaluation. Eng Struct 243:112652. https://doi.org/10.1016/j.engstruct.2021.112652 Kang F, Li JJ, Dai JH (2019) Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms. Adv Eng Softw 131:60–76. https://doi.org/10.1016/j.advengsoft.2019.03.003 Mata J (2011) Interpretation of concrete dam behavior with artificial neural network and multiple linear regression Models. Eng Struct 33(3):903–10. https://doi.org/10.1016/j.engstruct.2010.12.011 Li X, Wen ZP, Su HZ (2021) An approach using random forest intelligent algorithm to construct a monitoring model for dam safety. Eng Comput-Germany 37:39–56. https://doi.org/10.1007/s00366-019-00806-0 Wei BW, Chen LJ, Li HK et al (2020) Optimized prediction model for concrete dam displacement based on signal residual amendment. Appl Math Model 78:20–36 Yuan DY, Gu CS, Qin XN et al (2022) Performance-improved TSVR-based DHM model of super high arch dams using measured air temperature. Eng Struct 250:113400. https://doi.org/10.1016/j.engstruct.2021.113400 Mata J, Castro AT, Costa JS (2014) Constructing statistical models for arch dam deformation. Struct Control HLTH 21(3):423–7. https://doi.org/10.1002/stc.1575 Salazar F, Moran R, Toledo M´A et al (2017) Data-based models for the prediction of dam behaviour: a review and some methodological considerations. Arch Comput Method E 24(1):1–21 Cao EH, Bao TF, Gu CS, Li H, Liu YT, Hu SP (2020) A Novel Hybrid Decomposition—Ensemble Prediction Model for Dam Deformation. Appl Sci 10(16):5700. https://doi.org/10.3390/app10165700 Rankovi´c V, Grujovi´c N, Divac D, Milivojevi´c N (2014) Development of support vector regression identification model for prediction of dam structural behaviour. Struct Saf 48:33–9. https://doi.org/10.1016/j.strusafe.2014.02.004 Chen B, Hu TY, Huang ZS, Fang CH (2019) A spatio-temporal clustering and diagnosis method for concrete arch dams using deformation monitoring data. Struct Health Monit 18(5–6):1355–1371. https://doi.org/10.1177/1475921718797949 Chen SY, Gu CS, Lin CN et al (2021) Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement. Eng Comput-Germany 37:1943–1959. https://doi.org/10.1007/s00366-019-00924-9 Jeong S, Ferguson M, Hou R et al (2019) Sensor data reconstruction using bidirectional recurrent neural network with application to bridge monitoring. Adv Eng Inform 42:100991. https://doi.org/10.1016/j.aei.2019.100991 Gu H, Wang TF, Zhu YT et al (2021) A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces. Appl Sci 11:463. https://doi.org/10.3390/app11010463 Tian Y, Zhang KL, Li JY, Lin XX, Yang BL (2018) LSTM-based traffic flow prediction with missing data. Neurocomputing 318:297–305. https://doi.org/10.1016/j.neucom.2018.08.067 Sierra AJZ, Pinto AC, Montoya FG et al (2019) Wind missing data arrangement using wavelet based techniques for getting maximum likelihood. Energ Convers Manage 185:552–561. https://doi.org/10.1016/j.enconman.2019.01.109 Ma J, Cheng J, Ding YX et al (2020) Transfer learning for long-interval consecutive missing values imputation without external features in air pollution time series. Adv Eng Inform 44:101092. https://doi.org/10.1016/j.aei.2020.101092 Sahin UA, Bayat C, Ucan ON (2011) Application of cellular neural network (CNN) to the prediction of missing air pollutant data. Atmos Res 101(1–2):314–326. https://doi.org/10.1016/j.atmosres.2011.03.005 Che ZP, Purushotham S, Cho K, Sontag D, Liu Y (2018) Recurrent Neural Networks for Multivariate Time Series with Missing Values. Sci Rep 8(1):6085. https://doi.org/10.1038/s41598-018-24271-9 Ispirova G, Eftimov T, Seljak BK (2020) Evaluating missing value imputation methods for food composition databases. Food Chem Toxicol 141:111368. https://doi.org/10.1016/j.fct.2020.111368 Smarsly K, Law KH (2014) Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy. Adv Eng Softw 73:1–10. https://doi.org/10.1016/j.advengsoft.2014.02.005 Dragos K, Smarsly K (2016) Distributed adaptive diagnosis of sensor faults using structural response data. Smart Mater Struct 25(10):105019–105115. https://doi.org/10.1088/0964-1726/25/10/105019 Cheng J, Ma J (2015) A data-driven study of important climate factors on the achievement of LEED-EB credits. Build Environ 90:232–244. https://doi.org/10.1016/j.buildenv.2014.11.029 Myers TA (2011) Goodbye, likewise deletion: presenting hot deck imputation as an easy and effective tool for handling missing Data. Commun Methods Meas 5:297–310. https://doi.org/10.1080/19312458.2011.624490 Zhang Y, Zhou BH, Cai XR et al (2021) Missing value imputation in multivariate time series with end-to-end generative adversarial networks. Inform Sci 551:67–82. https://doi.org/10.1016/j.ins.2020.11.035 Ma J, Cheng J, Jiang FF et al (2020) A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data. Energ Buildings 216:109941. https://doi.org/10.1016/j.enbuild.2020.109941 Ouyang TH, Zha XM, Qin L (2017) A combined multivariate model for wind power prediction. Energ Convers Manage 144:361–373. https://doi.org/10.1016/j.enconman.2017.04.077 Nelwamondo FV, Mohamed S, Marwala T. (2007) Missing data: A comparison of neural network and expectation maximization techniques. Curr Sci India 93:11 https://www.researchgate.net/publication/1887898 Zhang SC (2012) Nearest neighbor selection for iteratively kNN imputation. J Syst Software 85(11):2541–2552. https://doi.org/10.1016/j.jss.2012.05.073 Li YT, Bao TF, Chen H et al (2021) A large-scale sensor missing data imputation framework for dams using deep learning and transfer learning strategy. Measurement 178:109377. https://doi.org/10.1016/j.measurement.2021.109377 Wei BW, Liu B, Yuan DY et al (2021) Spatiotemporal hybrid model for concrete arch dam deformation monitoring considering chaotic effect of residual series. Eng Struct 228:111488. https://doi.org/10.1016/j.engstruct.2020.111488 Stojanovic B, Milivojevic M, Milivojevic N, Antonijevic D (2016) A self-tuning system for dam behavior modeling based on evolving artificial neural Networks. Adv Eng Softw 97:85–95 Salazar F, Toledo MA, Oñate E, Morán R (2015) An empirical comparison of machine learning techniques for dam behaviour modelling. Struct Saf 56:9–17. https://doi.org/10.1016/j.strusafe.2015.05.001 Shu XS, Bao TF, Xu RC et al (2021) Dam anomaly assessment based on sequential variational autoencoder and evidence theory. Appl Math Model 98:576–594. https://doi.org/10.1016/j.apm.2021.05.021 Mao YC, Zhang JH, Qi H, Wang LB (2019) DNN-MVL: DNN-Multi-View-Learning-Based Recover Block Missing Data in a Dam Safety Monitoring System. Sensors 19(13):2895. https://doi.org/10.3390/s19132895 Wei BW, Yuan DY, Li HK, Xu ZK (2019) Combination forecast model for concrete dam displacement considering residual correction. Struct Health Monit 18(1):232–244. https://doi.org/10.1177/1475921717748608 Kang F, Li JJ, Zhao SZ, Wang YJ (2019) Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation. Eng Struct 180:642–653 Tatin M, Briffaut M, Dufour F et al (2018) Statistical modelling of thermal displacements for concrete dams: Influence of water temperature profile and dam Thickness Profile. Eng Struct 165:63–75. https://doi.org/10.1016/j.engstruct.2018.03.010 Alizadeh B, Bafti AG, Kamangir H et al (2021) A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction. J Hydrol 601:126526. https://doi.org/10.1016/j.jhydrol.2021.126526 Adnan RM, Mostafa RR, Kisi O (2021) Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization. Knowl-Based Syst 230:107379. https://doi.org/10.1016/j.knosys.2021.107379 Zhang T, Zheng XQ, Liu MX (2021) Multiscale attention-based LSTM for ship motion prediction. Ocean Eng 230:109066. https://doi.org/10.1016/j.oceaneng.2021.109066 Shi G, Qin CJ, Tao JF, Liu CL (2021) A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead Torque. Knowl-Based Syst 228:107213. https://doi.org/10.1016/j.knosys.2021.107213 Wang K, Ma CX, Qiao YH et al (2021) A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction. Physica A 583:126293. https://doi.org/10.1016/j.physa.2021.126293 Li YL, Min KY, Zhang Y, Wen LF (2021) Prediction of the failure point settlement in rockfill dams based on spatial-temporal data and multiple-monitoring-point models. Eng Struct 243:112658. https://doi.org/10.1016/j.engstruct.2021.112658 Hu YT, Mao HN, Mckenzie G (2019) A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements. Int J Geogr Inf Sci 33(4):714–738. https://doi.org/10.1080/13658816.2018.1458986 Yuan RY, Su C, Cao EH et al (2021) Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction. Appl Sci 11(16):7334. https://doi.org/10.3390/app11167334 Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: Theory and applications. Neurocomputing 70(1–3):489–501. https://doi.org/10.1016/j.neucom.2005.12.126 Yu P, Cao J, Jegatheesan V, Du XJ (2019) A Real-Time BOD Estimation Method in Wastewater Treatment Process Based on an Optimized Extreme Learning Machine. Appl Sci 9(3):523. https://doi.org/10.3390/app9030523 Huang YM, Li SS, Li JH et al (2019) Spectral diagnosis and defects prediction based on ELM during the GTAW of Al alloys. Measurement 136:405–414. https://doi.org/10.1016/j.measurement.2018.12.074 Shao CF, Gu CS, Yang M et al (2018) A novel model of dam displacement based on panel data. Struct Control HLTH 25:e2037. https://doi.org/10.1002/stc.2037 Shi ZW, Gu CS, Qin D (2016) Variable-intercept panel model for deformation zoning of a super-high arch dam. Springerplus 5:898. https://doi.org/10.1186/s40064-016-2600-z Dong W, Huang YM, Lehane B, Ma GW (2020) XGBoost algorithm-based prediction of concrete electrical resistivity forstructural health monitoring. Automat Constr 114:103155. https://doi.org/10.1016/j.autcon.2020.103155 Dai B, Gu CS, Zhao EF, Qin XN (2018) Statistical model optimized random forest regression model for concrete dam deformation monitoring. Struct Control HLTH 25:e2170. https://doi.org/10.1002/stc.2170 Chen ZY, Zheng DJ, Shen JX et al (2019) Research on distributed optical-fiber monitoring of biaxial-bending structural deformations. Measurement 140:462–471. https://doi.org/10.1016/j.measurement.2019.04.031