# An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
## 5 Conclusions  
This study proposed a data-driven EEMD-LSTM hybrid method to complement sensors' missing accurate measurement data. The method does not require the combination of finite element models but only pre-field measured data to recover and reconstruct the missing data. More specifically, the EEMD method was first used to decompose the original real measurement data of the sensor into several subseries, i.e., multiple IMFs and a residual, which reflect the local characteristics of the original real-measurement data at different time scales from high to low frequency. Then, the missing values were predicted by combining the LSTM with a deep learning algorithm and considering temporal correlation. The ability of the EEMD-LSTM model to recover the missing measured data was evaluated using the original measured acceleration data from a large LNG storage tank model in shaking-table experiments. The performance of this method was compared and discussed with traditional deep learning methods for missing measurement signal data complementation. The comparative analysis of the prediction evaluation metrics of the single deep learning methods and the enhanced hybrid EEMD methods, the comparative analysis of the performance of different deep learning algorithm models based on EEMD, and the performance of the proposed model on single-step, 5-step, 10-step, and 15-step predictions led to the following main conclusions. 1. Compared with the traditional single prediction models, the proposed EEMD-based methods well captured the micro change features of the measured signal, thereby obtaining a better data complementary performance;
2. The results of the measured signal data recovery showed that compared with EEMD-BiGRU, EEMD-GRU, and EEMD-DNN, the proposed EEMD-LSTM method could make full use of the long-term correlation of time series data and, thus, performed optimally in the missing data complementation performance indicators. It was also demonstrated that EEMD-LSTM could effectively correlate the historical information with the current input features and achieve accurate missing data completion;
3. The data recovery capability of most of the algorithms may decrease with the increase in missing data. The results showed that the model's accuracy decreased with the increasing in step size. It is worth mentioning that EEMD-LSTM had the best missing data complementation performance among all methods when the missing data increased. One of the limitations of the research work in this paper is that the approach was purely data-driven and did not consider the physical meaning in reality. Therefore, it is suggested that future work should be carried out on how to embed physical information into deep neural networks. Another research direction is to consider the latter part of the missing data and input that part of the data into the EEMD-based method as features. In addition, the spatial dependencies between sensors deserve further in-depth study. Conceptualization, Z.C. and L.Z. ; methodology, L.Z. and K.L. ; software, C.Y. and L.Z. ; validation, Z.C., C.Y. and X.X. ; formal analysis, C.Y. and L.Z. ; investigation, C.Y. and H.W. ; resources, K.L. and L.W. ; data curation, H.W. ; writing--original draft preparation, C.Y. and L.Z. ; writing--review and editing, Z.C. and K.L. ; visualization, H.W. and X.X. ; supervision, Z.C. and X.X. ; project administration, Z.C. and L.W. ; funding acquisition, Z.C. and L.W. All authors have read and agreed to the published version of the manuscript. The authors appreciate the testing facility as well as the technical assistance provided by the CLP Power Wind/Wave Tunnel Facility at the Hong Kong University of Science and Technology. This work was funded by the Key Laboratory of Icing and Anti/De-icing of CARDC (Grant No. IADL 20200304), the National Natural Science Foundation of China (Grant No. 51908090), the Fundamental Research Funds for the Central Universities (Project No. 2021 CDJQY-001 and 2022 CDJX-016), the Natural Science Foundation of Chongqing, China (Grant No.