## 5 Conclusions  
The RSSI parameter is one of the most important parameters for hybrid FSO/RF systems. Thanks to it, we can correctly configure the hard switching method and decide which line will transmit at a given time. In this work, two machine learning methods for RSSI parameter prediction were tested. It was a decision tree method and a decision tree method using the AdaBoost Regressor function. If we want to correctly predict the value of the RSSI parameter, we must correctly understand the dependence of this parameter on atmospheric conditions. These were recorded  
Figure 16: Comparison of real RSSI values and values predicted by AdaBoost regressor trained from larger amount of variable data + partial amount of real data from predicted period (4896 values and 10932 records), averaged values of temperature and wind speed. Figure 15: Comparison of real RSSI values and values predicted by AdaBoost regressor trained from a larger amount of variable data + a partial amount of real data from the predicted period. using a weather monitoring station located at one of the FSO heads and a UAV device. Based on these parameters, correlations were found between the individual parameters and the RSSI value. Subsequently, we proceeded to implement the machine learning program itself using the method of decision trees. When we used training:test ratio 80:20 data distribution, we achieved high training speed and good results, but in other more detailed tests it was not so successful. The second implemented program was based on the decision tree algorithm improved by AdaBoost regression. The results obtained by this machine learning algorithm showed a higher prediction efficiency than the decision trees themselves, but also the length of training and improvement of such a model was many times higher. We tried to train the models with different input data configurations--we only tried to use data from every second, third, fifth, or tenth minute--and then we analyzed the success of the prediction. In targeted testing of the prediction in highly variable data, we found that the success of the prediction did not reach the desired value, as the set of input training data mostly contained similar values of individual parameters (nice weather) and peaks (rain, snow, fog) occurred in it only to a small extent. Therefore, we tried to train algorithms with a data set containing reduced redundant data. In this case, the success of the algorithms managed to reach a higher level, but it still did not reach optimal values. If we pushed part of the real values from the predicted period into the algorithm, the efficiency of the prediction around these values increased significantly. From this, we concluded that it is very important when training the algorithms to make sure that the input data set is sufficiently variable and covers all possible states to approximately the same extent. This information could serve as a basis for further future research on this issue. To achieve even better results and high efficiency of prediction, it would be appropriate to consider the prediction of weather parameters and, thus, examine the behavior of the decision tree algorithm. Since the decision tree is the basis of the random forest algorithm, in further research we would like to focus on this algorithm and examine its prediction efficiency. Conceptualization, M.L. and L.O. ; methodology, M.L. ; software, M.L. ; validation, M.L., L.O., J.O. and N.Z. ; formal analysis, M.L. ; investigation, M.L. ; resources, M.L., L.O., J.O. and N.Z. ; data curation, M.L. ; writing--original draft preparation, M.L. ; writing--review and editing, M.L., L.O., J.O. and N.Z. ; visualization, M.L. ; supervision, L.O. and J.O. ; project administration, L.O. and J.O. ; funding acquisition, L.O. All authors have read and agreed to the published version of the manuscript. This work was supported by the research project VEGA 1/0584/20 \"Person Monitoring by UWB Sensor Systems Operating in Real Conditions\". Not applicable.