## 4 Conclusions  
In this research, a trans-disciplinary investigation was carried out, for seismic prediction via machine learning technique. Several machine learning algorithms--including deep neural networks, random forest, support vector machine, and linear regressions--were applied to three distinct datasets: Chile, Southern California, and Hindukush. The algorithms were evaluated on the mentioned datasets, using in-sample and out-of-sample techniques. Our proposed deep neural network approach outperformed the benchmark techniques on all the datasets. On average, the accuracy of the proposed model was 10% better than the benchmark algorithms, while in terms of precision, recall, and F1-score, the performance improvement was 20%, 29%, and 36%, respectively. The same performance order was observed using out-of-sample testing. Although our model outperformed the existing works, and achieved significantly better performance, it is important to mention that we focused on seismic activity prediction only (classification problem), and did not consider the problem of predicting exact magnitude (regression problem). Furthermore, we did not collect our own data: instead, we used the currently available and already peer-reviewed data. Like all machine learning works, the current work was heavily dependent on the underlying data. Although we have shown that our proposed model was able to achieve better performance than the current techniques, the model may require further testing on other datasets, to strengthen its case. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Region: Chile**} \\ \hline
**Authors** & **Accuracy** & **Precision** & **Recall** \\ \hline [PERSON] et al. [19] & 75.2 & 71 & 71 \\ [PERSON] et al. [11] & 86.6 & 84.2 & 94.4 \\ [PERSON] et al. [9] & 90.6 & 93.8 & 98.7 \\ Proposed Model & 99.26 & 99.29 & 95.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison of extant studies to proposed model for Southern California region. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Region: Chile**} \\ \hline
**Authors** & **Accuracy** & **Precision** & **Recall** \\ \hline [PERSON] et al. [20] & 79.7 & 61.1 & 89.2 \\ [PERSON] et.al. [11] & 84.5 & 80.2 & 93.9 \\ [PERSON] et.al. [9] & 84.9 & 73.2 & 90.5 \\ Proposed Model & 98.28 & 98.69 & 98.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of extant studies to proposed model for Chile region. Finally, like all machine learning techniques, our proposed model also suffers from the inherit risk of interpretability. The proposed technique enriches the body of literature, by proposing a deep-learning-based generalizable architecture for seismic activity prediction. The study should be of interest to trans-disciplinary researchers and practitioners in the domain of seismology. This work could be extended further, by identifying the important features that affected the seismic outcome. Another interesting direction would be to design explainable AI-based techniques. Conceptualization, I.A. and D.M. ; methodology, D.M., M.O.A. and W.K. ; software, D.M., I.A. and M.I.K. ; validation, M.I.K. and W.K. ; formal analysis, M.O.A. and D.M. ; investigation, D.M. and I.A. ; resources, I.A. and M.O.A. ; data curation, D.M., M.I.K. and W.K. ; writing--original draft preparation, D.M. and I.A. ; writing--review and editing, M.I.K., W.K. and M.O.A. ; visualization, D.M. ; supervision, I.A. ; project administration, I.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data is available at [[https://figshare.com/articles/dataset/Earthquake_Prediction_using_SVR_and_HNN/6406814/1](https://figshare.com/articles/dataset/Earthquake_Prediction_using_SVR_and_HNN/6406814/1)]([https://figshare.com/articles/dataset/Earthquake_Prediction_using_SVR_and_HNN/6406814/1](https://figshare.com/articles/dataset/Earthquake_Prediction_using_SVR_and_HNN/6406814/1)) (accessed on 15 January 2023).