**6. Conclusion**

This chapter deals with the estimation of seismic risk given by the temporal ETAS conditional intensity function. To achieve this goal, two deep learning models were implemented: a deep feedforward artificial neural network and a recurrent long short-term memory network. The results show a good estimation, in particular with the DFANN model. However, it should be pointed out that both implemented models could be improved by adding more hidden layers or stacking more LSTM layers in the DFANN and RNN-LSTM models, respectively. Also, exogenous variables (such as ground motion among others) could be considered for improving the predictions. Since the proposed model only considers a temporal model, extensions to the prediction of earthquake locations will be considered in future works. We think that deep learning algorithms could be useful tools for many earthquake prediction approaches.
