# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
## 6 Conclusions and Future Work  
Natural disasters like earthquakes can be very damaging. Automatic early earthquake detection using the data from seismic station sensors has emerged as an important area of research in recent years for emergency response. This article proposes a stacked-base ensemble method SNRNN for earthquake detection. This model, an ensemble of RNN, GRU, and LSTM base models, incorporating layer normalization techniques, can successfully detect the depth and magnitude of an earthquake event. After the data were preprocessed, the RNN, GRU, and LSTM sequentially extracted the feature map to make the final detection. Batch and layer normalization were utilized to achieve more consistent and faster training. Layer normalization was used to normalize features independently of the batch size. Therefore, layer normalization is more effective than batch normalization when using RNN-based models. We applied RNN, GRU, and LSTM models independently with both normalization methods. But the performance was lower than the proposed SNRNN model. After that, these models were ensembled to design a powerful model. We tested the proposed model on 6574 earthquake events from 2000 to 2018 in Turkey. The proposed model achieves 3.16 and 3.24 RMSE values for magnitude and depth detection. The RNN models outperform layer normalization over batch normalization. We also conclude that the ensemble model outperforms the individual model.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Magnitude** & **Depth** \\ \hline SimpleRNN & 4.87 & 5.52 \\ GRU & 5.41 & 5.12 \\ LSTM & 4.35 & 4.84 \\ SimpleRNN+BN & 4.32 & 5.5 \\ SimpleRNN+LN & 4.28 & 5.44 \\ GRU+BN & 5.42 & 5.24 \\ GRU+LN & 5.38 & 5.02 \\ LSTM+BN & 4.31 & 4.79 \\ LSTM+LN & 4.14 & 4.65 \\ SimpleRNN+GRU & 4.31 & 4.43 \\ SimpleRNN+LSTM & 3.78 & 4.32 \\ GRU+LSTM & 3.89 & 4.19 \\ SimpleRNN+GRU+LSTM & 3.27 & 3.65 \\ SimpleRNN+GRU+NL & 4.28 & 4.13 \\ SimpleRNN+LSTM+NL & 3.57 & 4.1 \\ GRU+LSTM+NL & 3.45 & 3.38 \\
**SimpleRNN+GRU+LSTM+NL** & **3.16** & **3.24** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the baseline models and the proposed SNRNN model.  
Researchers, seismologists, and the metrological department will all benefit from this innovative SNRNN by learning more about the potential of the ensemble method and how to apply many data mining techniques at once. In the future, we plan to apply similar ensemble techniques for earthquake detection with homogenous deep learning models. Further, the proposed model can be used for ground motion intensity detection.  
Conceptualization, Y.J. and Y.W.; Formal analysis, M.A.B., M.P.A. and H.L.; Funding acquisition, Y.J. and Y.W.; Investigation, H.L.; Methodology, M.P.A.; Software, M.A.B.; Supervision, H.L.; Visualization, M.A.B.; Writing--original draft, M.A.B.; Writing--review and editing, M.P.A. All authors have read and agreed to the published version of the manuscript.  
National Key R&D Plan: 2021 YFC2901801.  
Not applicable.  
Dateset was collected from AFAD. The data is Publically available on AFAD website.  
The authors declare no conflict of interest.