*4.4. Comparison with Other Machine Learning Models*

This section compares the results of this model with the results of other models with the same objective. Following a literature review, it is concluded that [24] is the closest reference to the model being used in this paper. Firstly, the indicators of the model are presented to compare the overall performance. In Table 6, the maximum and minimum values are presented. This may allow a comparison of evaluation results for models with the same objective.

**Table 6.** Minimum and maximum indicators of [24] model.


This indicates that the two models have a very similar tier of performance. In Table 6, the minimum and maximum are presented, because in [24] several algorithms are tested.

In terms of explainability, this model is mainly influenced by the time components. In the model of [24], the main variable is the timestamp. In this respect, the two models coincide. The next most important variables are the Entry Count for the next 60 min, the capacity, and the Entry Count for the next 20 min. From this point on, workload and traffic distribution variables start to appear. The Entry Count is analogous to the number of aircraft, and the traffic distribution variables would be similar to the flow distribution in this model.

With these two comparisons, although with different approaches, the two models arrive at similar results. This allows us to conclude that the model developed in this paper is correct, having been tested against a robust model with prestige within academia.
