**5. Discussion**

*Natural Hazards - Risk, Exposure, Response, and Resilience*

*Structure for the DL models, for both DFANN (on the left) and RNN-LSTM (on the right).*

**Mag DFANN RNN-LSTM** >3 **0.3478/0.2603** 0.5651/0.5167 >4 0.4624/0.3440 0.6698/0.4732 >5 0.5894/0.4457 0.7572/0.4449 >6 0.4226/0.4654 0.7941/0.4741

*Root mean square error (RMSE) of the training and test groups for each DFANN and RNN-LSTM deep* 

**10**

**Table 1.**

*learning models.*

**Figure 6.**

**Figure 7.**

**RMSE training/test**

*Training and test groups for the best model (DFANN, Mag > 3).*

*In bold the best model.*

This work introduces a novel approach to predict the temporal ETAS-GIF alternative to the statistical approach proposed by [14]. The deep learning method has recently been used for predicting locations of aftershock events [31] especially based on ground motion data. The first use of a feedforward neural network for the prediction of seismic hazard was introduced by [32] in the spatial domain.

Possible extensions of the deep learning approach could be to include the ground motion together to other variables [30, 31] as inputs of the model and to incorporate the spatial dimension for a spatiotemporal prediction [33–35]. Some statistical techniques could be used for identifying possible patterns and inputs [36–37].

Also, since seismic events could be characterized by different features depending of the different locations of the principal events, we think that DL neural network models could be used for characterizing earthquakes in some specific seismic areas such as the local ETAS models [7, 11].

Different neural networks models could be used for comparing earthquake predictions [38]. For example, Bayesian DL neural networks could be used for a new prediction scenario considering the uncertainty of major earthquake occurrences and the probability of recurrence in a similar way to the Bayesian approach proposed by [32]. Additionally, other DL and machine learning approaches as convolutional neural networks (CNN), generative networks (GN), and random forest regression (RFR) could be implemented by incorporating the spatial component and allowing to "generate" new prediction seismic risk maps.

However, the main limitation of neural networks is that they are considered "black boxes" since it is difficult to quantify the correlation between the involved variables and their uncertainty.
