## 4 Experiments and Results
### Performance Evaluation
this causes the DNN to fail in the prediction. Nevertheless, the F1-scores for all months maintain a high level, which proves the feasibility of the DNN. Precision and recall have similar values. A higher F1-score is caused by higher precision and recall values, and vice versa. The accuracy values are also high and similar to the F1-score values. Finally, we notice that for the whole dataset, the best MAE of 0.03059 is obtained. Table 6 presents the evaluation of the DNN in every category using the precision, recall, and F1-scores for 10 independent runs. The DNN performs well in the classification task for all categories. The best per-class F1-scores are obtained for different types of categories: a very reduced number of beach visitors (0-49), a very high number (300+), and a medium number (150-199). The categories 50-99 and 200-249 show the worst results for the F1-score due to the increase of false negatives (recall). The category 100-149 has an F1-score of 89.2% due to the increase of the false positives (precision). Nevertheless, the precision rate, recall, and F1-score are relatively stable for all categories. The performance of the proposed DNN for the whole dataset was investigated with different batch sizes. The results of 10 independent runs are shown in Table 7. With a batch size of 64, the best F1-score of 92.3412% is achieved. In order to check the robustness of the results, the model was evaluated for the time periods 2018-2020 and 2019-2021. Tables 8 and 9 present the evaluation of the DNN in every category using the precision, recall, and F1-scores for the selected time periods and 10 independent runs. The DNN performs well in the classification task for all categories. In the time period 2018-2020, the best per-class F1-scores are obtained for different types of categories: a reduced number of beach visitors (50-99) and a high/very high number (250-299)/(300+). The category 100-149 shows the worst results for the F1-score due to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **F1-Score** \\ \hline
**0–49** & 0.981 & 0.981 & **0.982** \\
**50–99** & 0.943 & 0.898 & 0.919 \\
**100–149** & 0.865 & 0.925 & 0.892 \\
**150–199** & 0.92 & 0.956 & **0.9323** \\
**200–249** & 0.925 & 0.849 & 0.879 \\
**250–299** & 0.939 & 0.914 & 0.926 \\
**300+** & 0.93 & 0.97 & **0.949** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Per-class precision, recall, and F1-scores for the time period 2016-2018. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Size** & **MAE** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
**June** & 0.0553 & 0.84298 & 0.844526 & 0.836719 & 0.837623 \\
**July** & 0.03069 & 0.94219 & 0.935523 & 0.935185 & 0.934539 \\
**August** & 0.04055 & 0.94801 & 0.946325 & 0.944 & 0.943866 \\
**whole dataset** & **0.03059** & **0.9269333** & **0.929422** & **0.926933** & **0.927114** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MAE, accuracy, precision, recall, and F1-score. the increase of false negatives (recall). The category 200-249 has an F1-score of 90% due to the increase of the false positives (precision). In the time period 2019-2021, the best per-class F1-scores are obtained for categories with a high/very high number of beach visitors (200-249; 250-299)/(300+). The categories 100-149 and 150-199 show the worst results for the F1-score due to the increase of false negatives (recall). We observe that for both time periods, the precision rate, recall, and F1-score are relatively stable for all categories. Therefore, we can conclude that the robustness of the model is not affected by the time periods chosen.