## 4 Discussion and Conclusions  
The results obtained by these models show that model 3, created using the third dataset variant containing historical forecasted data, achieved the best overall results over the training data, achieving a 0.84 average precision. The third dataset variant also achieved the best overall results during testing, obtaining a 0.86 average precision, while models 1 and 2 obtained a 0.67 and 0.48 average precision, respectively. Comparing models 1 and 3 using the model 3's testing dataset, i.e., forecasted data more similar to that used in production, also shows that model 3 outperforms model 1. Analysing these results, we can conclude that the historical forecasted data creates the best wave overtopping prediction model (model 3), i.e., creating a model using a dataset with data from the same data distribution as the data the model will use in production provides the best results, as initially hypothesised. The results obtained by the ANNs overtopping prediction models are satisfactory but can be improved by obtaining more data. As previously explained, the overtopping datasets are imbalanced as there are significantly fewer instances with overtopping events than without them. Gathering more data would help improve the overall results by avoiding overfitting and reducing the need for regularisation. Not needing regularisation could accelerate the training process, which currently takes several days for each model. However, gathering more data and creating new models is a slow process, as can be observed by the time it took to create the models that include the last processed data from the 2020 field campaign. The extensive time it takes to develop the models can be attributed to several factors, with data collection and preprocessing complexity being the main contributing factors. However, the iterative model development and validation also increase the time. The overtopping recording devices were installed in the illumination towers of the port, the closest fixed power source. However, to detect more events, some locations should be closer to the breakwater to visualise overtopping events in adverse meteorological conditions, such as heavy rain or dense fog. This situation cannot be easily improved, but we could improve the overtopping detection method to speed up the process and gather data faster. Currently, detecting them involves the following tasks:  
1. Periodically collecting the videos stored in the recording devices. 2. Inspecting the videos manually and annotating each overtopping event that can be visually detected. This method of working has proven to be highly time consuming. The time and resources involved in the first task can be reduced by overcoming the lack of communication infrastructure in the outer port by installing 4G or satellite modems at the recording sites. This infrastructure would allow data to be sent in real time and the videos to be immediately available for processing. The complexity continues after data collection. Each field campaign gathers tens of terabytes of video (multiple cameras during a year) that must be manually inspected to detect and annotate each overtopping event. This manual process is highly time-consuming (more than a year for each field campaign) and the main bottleneck in the data preparation phase. This task could be sped up by creating a Machine Learning model that automatically processes the videos without human interference and detects the over-topping events. In recent years, deep learning models have dramatically improved the performance of this type of task (image classification). Although creating such models involves initially, and mostly manually, creating large image datasets, we already have the videos of the overtopping events we detected. Also, the time involved in creating such a dataset would far outweigh the time involved in manually inspecting the videos to detect the overtopping events.