**5. Conclusions and Future Work**

Once the machine learning model has been validated, it can be concluded that the results are satisfactory and that it is indeed possible to predict when a sector will or will not be regulated based on mainly temporal components. This model will not decrease the number of regulations directly, as it simply evaluates and predicts based on the situation within the sector and the time of year. The realisation of this model can present advantages in the management of the technological and human resources of the ATC system. By applying this model before the actual operation, the ATC system will be able to see which sectors will be regulated at which time and be able to dedicate more or fewer resources to the more or less regulated areas, allowing the ATC system to be more efficient in its labour.

Several conclusions can be drawn from the design of the model and its application.


In addition to the general conclusions of the model, the computational time allows conclusions to be drawn about the feasibility of the model, and the possibility of its implementation in a real case study. This model has been trained and tested in a time of 4 min, having been developed on a general computer. This time is practically immediate for such a large volume of data. When running a new application of the model, the prediction of a full day (144 elements) has been completed in just 2 s. These application times mean that the model can be implemented in a real tool. Moreover, although the results obtained have been good, there is interest in continuing this line of research. Future lines of research that would be interesting for the full development of the model are set out below.


In general, in order to have an operational validation of the methodology, more comparative studies will be needed. Such studies will be pursued in the future when more data is available to apply the methodology in different sectors, or when another methodology is available to compare the results with.

**Author Contributions:** Conceptualization, V.F.G.C.; methodology, F.P.M.; software, R.D.-A.J.; validation, R.D.-A.J.; formal analysis, M.Z.S.; investigation, R.M.A.V.; writing—original draft preparation, F.P.M.; writing—review and editing, M.Z.S.; supervision, R.M.A.V.; project administration, V.F.G.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by ENAIRE.

**Acknowledgments:** Acknowledgement to ENAIRE and CRIDA for the collaboration and funding of the project in which this research paper has been developed. I would also like to express my gratitude to CRIDA for providing the data necessary to carry out the work and obtain the results.

**Conflicts of Interest:** The authors declare no conflict of interest.
