**3. Methodology**

Once the motivation and objectives of this paper have been stated, the methodology in which the prediction of regulations in airspace is presented. This paper builds on some previous works, such as [23], where the possibility of predicting delay in the presence of airspace capacity regulations in the airspace is discussed. However, in this paper, the aim is to go further, looking directly at the cause rather than the consequence.

In [22], the authors also propose a prediction of ATFCM regulations, but this prediction is of weather-based regulations. Even so, part of the methodology is applicable. These same authors adapt this prediction of regulations to capacity regulations in [24]. This model is based on predictions of capacity regulations, which are also based on the traffic situation and evolution in the sector, as well as on the controller's workload. To complete the study of this work, it has been decided to use complementary variables to this study. Specifically, it has been decided to eliminate the variables related to the workload of the controllers, as this variable is subjective and subject to a certain model [25]. In addition, it has been decided to structure the air traffic variables in the main traffic flows. Therefore, the model will have a limited number of variables, but the idea is that these variables will give a complete picture of how the traffic is structured.

For this reason, in this paper, the aim is to make a prediction of capacity regulations based on objective data. The data which will be used in the machine learning model is:


Therefore, the proposed methodology will be based on three main areas, and Figure 1 shows the scheme that will be followed for the development of the machine learning model. As schematised in Figure 1, the model is based on three information inputs:


**Figure 1.** Scheme of the methodology of the machine learning model establishment.

In the following, it is detailed how each of the branches of the model is worked with and how they will be combined to form the complete machine learning model.
