## 3 Results

Table 6: Classification report for the three wave overtopping models using a 0.25, 0.5, and 0.75 threshold decision boundary on the testing data predictions. Figure 5: PR curves for the wave overtopping models 1 and 3 using the testing dataset of model 3 (historical forecasted data). Figure 5 shows that model 3 outperforms model 1 with an AP of 0.86 over its testing data, while model 1 obtains an AP of 0.71 using model 3's testing data. Table 7 shows the confusion matrices for models 1 and 3 obtained using the 0.25, 0.5, and 0.75 threshold values as the decision boundary on the predictions over the testing dataset of model 3. Table 7 shows that model 1 performs slightly better than model 3 regarding recall over model 3's testing data using the 0.25 and 0.5 thresholds, but model 3 outperforms model 1 overall. This affirmation is confirmed in Table 8, which shows that model 3 generally obtains higher macro precision, recall, and F\({}_{1}\) score, except for recall using the 0.25 threshold. Model latency refers to the delay between when input data are provided to a model and when the model outputs a prediction. In the context of the wave overtopping prediction models, model latency could be a significant concern for several reasons. In a port environment, timely decisions are crucial for taking preventative measures and optimizing operations when predicting a potential risk. High latency in model predictions could delay the response time, reducing the effectiveness of these measures and potentially compromising port safety and efficiency. Also, port operators and managers expect prompt and accurate predictions to facilitate informed decision making. Any delay in receiving predictions could diminish stakeholder trust in the models, affecting their adoption and continued use. For the models to be integrated seamlessly into port management systems and decision-making processes, they must operate within the time constraints of those systems. High latency could hinder the integration of these models into existing operational workflows, limiting their practical utility. To address these concerns, we calculated the latency of model 3, simulating a production environment:  
1. We create a dataset of 72 inputs, the same number the model will use in production. 2. We calculated the model latency for the dataset (predicting 72 outputs). 3. We ran step 2 10,000 times and calculated the mean latency. The simulations were run on a laptop (12 th Gen Intel(r) Core(tm) i7-1260P (Intel, Santa Clara, CA, USA)). \begin{table}
\begin{tabular}{c c|c c|c c|c c|c c} \hline \hline  & \multicolumn{4}{c|}{**Precision**} & \multicolumn{4}{c|}{**Recall**} & \multicolumn{4}{c|}{**F\({}_{1}\)**} & \multicolumn{4}{c|}{**Accuracy**} & \\ \hline
**Model** & **Threshold** & **0.25** & **0.50** & **0.75** & **0.25** & **0.50** & **0.75** & **0.25** & **0.50** & **0.75** & **Support** \\ \hline \multirow{4}{*}{1} & No overtop. & 1.00 & 0.99 & 0.99 & 0.98 & 1.00 & 1.00 & 0.99 & 0.99 & & & 3710 \\  & Overtop. & 0.37 & 0.94 & 0.00 & 0.89 & 0.27 & 0.00 & 0.52 & 0.42 & 0.00 & & & 55 \\  & Macro avg & 0.68 & 0.96 & 0.49 & 0.93 & 0.64 & 0.50 & 0.75 & 0.71 & 0.50 & 0.98 & 0.99 & 0.99 & 3765 \\  & Weigh. avg & 0.99 & 0.99 & 0.97 & 0.98 & 0.99 & 0.99 & 0.98 & 0.99 & 0.98 & & & 3765 \\ \hline \multirow{4}{*}{3} & No overtop. & 1.00 & 1.00 & 0.99 & 0.99 & 1.00 & 1.00 & 0.99 & 1.00 & 1.00 & & & 3710 \\  & Overtop. & 0.54 & 0.95 & 0.96 & 0.85 & 0.73 & 0.47 & 0.66 & 0.82 & 0.63 & & & 55 \\ \cline{1-1}  & Macro avg & 0.77 & 0.97 & 0.98 & 0.92 & 0.86 & 0.74 & 0.83 & 0.91 & 0.82 & 0.99 & 1.00 & 0.99 & 3765 \\ \cline{1-1}  & Weigh. avg & 0.99 & 1.00 & 0.99 & 0.99 & 1.00 & 0.99 & 0.99 & 1.00 & 0.99 & & & 3765 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification report for the wave overtopping models 1 and 3 using a 0.25, 0.5, and 0.75 threshold decision boundary on the testing data predictions.