# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
## 6 Conclusions and Future Work
The authors declare that no conflict interest for the present study was found. The predicted results for A_2 data sets using seven algorithms, including EMD-BiGRU, EMD-GRU, BiGRU, GRU, GBR, RFR, SVR, are shown in Table 1.  
Figure 11 shows the prediction results for the A_2 data set using different comparison algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**data set** & **Point** & **Metrics** & **EMD-BiGRU** & **EMD-GRU** & **BiGRU** & **GRU** & **GBR** & **RFR** & **SVR** \\ \hline A\_2 & 5 & MSE & 0.0065 & 0.0097 & 0.0249 & 0.0249 & 0.0573 & 0.0669 & 0.0429 \\  & 5 & RMSE & 0.0808 & 0.0987 & 0.1579 & 0.1579 & 0.2394 & 0.2587 & 0.2070 \\  & 5 & MAE & 0.0609 & 0.0739 & 0.1175 & 0.1182 & 0.1859 & 0.2010 & 0.1595 \\  & 5 & R\({}^{2}\) & 96.85\% & 95.30\% & 87.97\% & 87.96\% & 72.35\% & 67.72\% & 79.32\% \\ \hline A\_2 & 10 & MSE & 0.0054 & 0.0083 & 0.0156 & 0.0203 & 0.0841 & 0.0935 & 0.0679 \\  & 10 & RMSE & 0.0731 & 0.0910 & 0.1250 & 0.1424 & 0.2900 & 0.3058 & 0.2606 \\  & 10 & MAE & 0.0567 & 0.0711 & 0.0957 & 0.1090 & 0.2259 & 0.2386 & 0.2009 \\  & 10 & R\({}^{2}\) & 97.44\% & 96.03\% & 92.51\% & 90.29\% & 59.68\% & 55.17\% & 67.45\% \\ \hline A\_2 & 15 & MSE & 0.0073 & 0.0129 & 0.0200 & 0.0181 & 0.0919 & 0.0992 & 0.0777 \\  & 15 & RMSE & 0.0856 & 0.1136 & 0.1415 & 0.1345 & 0.3031 & 0.3149 & 0.2787 \\  & 15 & MAE & 0.0674 & 0.0887 & 0.1096 & 0.1049 & 0.2375 & 0.2476 & 0.2170 \\  & 15 & R\({}^{2}\) & 96.48\% & 93.78\% & 90.36\% & 91.29\% & 55.77\% & 52.25\% & 62.60\% \\ \hline A\_2 & 20 & MSE & 0.0047 & 0.0086 & 0.0829 & 0.0192 & 0.1026 & 0.1091 & 0.0904 \\  & 20 & RMSE & 0.0684 & 0.0929 & 0.2880 & 0.1384 & 0.3203 & 0.3302 & 0.3007 \\  & 20 & MAE & 0.0534 & 0.0727 & 0.2233 & 0.1079 & 0.2524 & 0.2609 & 0.2351 \\  & 20 & R\({}^{2}\) & 97.76\% & 95.87\% & 60.33\% & 90.84\% & 50.92\% & 47.84\% & 56.76\% \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison results for data set A_2 using different algorithms. The predicted results for A_3 data sets using seven algorithms, including EMD-BiGRU, EMD-GRU, BiGRU, GRU, GBR, RFR, SVR, which are shown in Table 2.  
The predicted results for A_4 data sets using seven algorithms, including EMD-BiGRU, EMD-GRU, BiGRU, GRU, GBR, RFR, SVR, which are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**data set** & **Point** & **Metrics** & **EMD-BiGRU** & **EMD-GRU** & **BiGRU** & **GRU** & **GBR** & **RFR** & **SVR** \\ \hline A\_4 & 5 & MSE & 0.0041 & 0.0164 & 0.0187 & 0.0180 & 0.0440 & 0.0495 & 0.035 \\  & 5 & RMSE & 0.0644 & 0.1281 & 0.1367 & 0.1343 & 0.2097 & 0.2226 & 0.187 \\  & 5 & MAE & 0.0470 & 0.0912 & 0.0957 & 0.0938 & 0.1597 & 0.1690 & 0.1414 \\  & 5 & R\({}^{2}\) & 97.26\% & 89.14\% & 87.64\% & 88.07\% & 70.91\% & 67.21\% & 76.87\% \\ \hline A\_4 & 10 & MSE & 0.0032 & 0.0039 & 0.0518 & 0.0107 & 0.0728 & 0.0770 & 0.0647 \\  & 10 & RMSE & 0.0562 & 0.0622 & 0.0227 & 0.1036 & 0.2698 & 0.2774 & 0.2543 \\  & 10 & MAE & 0.0427 & 0.0471 & 0.1649 & 0.0780 & 0.2063 & 0. Figure 13 shows the prediction results for data set A_4 using different comparison algorithms.