## 4 Discussion and Conclusions
Besides improving the throughput of each task with these improvements, allowing us to grow the overtopping dataset faster, the synergy of both would allow new capabilities: the communication infrastructure would allow us to have a real-time video feed of the recording devices, and using this as input of the overtopping classification model developed for the second task would create an overtopping warning system that will inform port operators of an overtopping event in real-time. As previously indicated, the iterative model development and validation also increase the model creation time. Developing the final models involves the previously explained iterative process of defining hyperparameters, training numerous models, and validating their performance through cross-validation. This iterative process, necessary for optimising model performance, is inherently time consuming, taking several months in our technological infrastructure, particularly in this work, where we trained the same models three times (one per dataset). Now that we know which model architecture and dataset provide the best results, the model training time can be significantly reduced in future works. Another limitation of these results concerns the ANN-based models used to predict overtopping events, which is model interpretability, i.e., explaining the reason behind a specific model's output. The machine learning models created in this work are intended to replace traditional statistical methods used in port management tools. These traditional models are interpretable, and port operators are accustomed to this characteristic. Using Machine Learning models, we cannot explain to port operators how and why the model came to the decision it did. The only tool a machine learning practitioner has in this situation is to convince through results, i.e., the models need to prove that they are superior to traditional statistical methods by providing more accurate predictions than traditional models. During the development of this work, and after obtaining the results, we had several meetings with the A Coruna port authority and the port operators. After reviewing the results obtained in this work, they are convinced that Machine Learning methods provide better results than traditional methods despite the lack of interpretability, so they intend to use the models created in this work in their decision-making pipeline. In conclusion, analysing the results of the overtopping prediction ANNs and the video recording infrastructure's ability to collect data for years without significant problems makes us conclude that the in situ measuring approach was appropriate, and the models created using the data gathered provided good results. Also, using historical forecasted data to create the models provides the best results, so this is the best approach, which we will focus on in future works. Conceptualization, A.F., E.P., A.A. and J.S. ; methodology, A.A. and A.F. ; software, A.A. and S.R.-Y. ; validation, A.F., E.P., A.A. and J.S. ; formal analysis, A.F., E.P., A.A. and J.S. ; investigation, A.A. and A.F. ; resources, A.A., A.F. and E.P. ; data curation, A.A. and A.F. ; writing--original draft preparation, A.A. and P.R.-S.; writing--review and editing, P.R.-S., A.F., J.S. and A.A.; visualization, S.R.-Y., P.R.-S., E.P. and A.F. ; supervision, A.F., J.R. and E.P. ; project administration, A.F., J.R. and E.P. ; funding acquisition, J.R. and E.P. All authors have read and agreed to the published version of the manuscript. This research was funded by the Spanish Ministry of Science and Innovation [grant number PID2020-112794 RB-I00, funded by MCIN/AEI/10.13039/501100011033]. Not applicable. The datasets presented in this article are not readily available to protect the confidentiality and security of the Outer Port of A Coruna. Requests to access the datasets should be directed to the corresponding author, and the data will be sent with the permission of the Port Authority.