## 4 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training with Data from Each** & **Length of the Decision Tree Training Process** & **Training Process** & **Decision Tree** & **AdaBoost Prediction** \\  & **Tree Training Process** & **Length (AdaBoost)** & **Prediction Efficiency** & **Efficiency** \\ \hline
1 min & 0.68 s & 2:27 min & 81\% & 92\% \\
2 min & 0.34 s & 1:06 min & 76\% & 86\% \\
3 min & 0.23 s & 0:41 min & 78\% & 88\% \\
5 min & 0.14 s & 0:22 min & 56\% & 73\% \\
10 min & 0.08 s & 0:10 min & 15\% & 56\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of individual algorithms (120,000 records, n_estimators = 300, training ratio 80:20)—data used: temperature 1, 2, and 3; pressure; humidity; wind speeds 1 and 2; particle concentration, visibility. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Y** & \(\mathbf{p_{P}}\) **(RSSI, Y)** & \(\mathbf{\rho_{s}}\) **(RSSI, Y)** & \(\mathbf{\rho_{k}}\) **(RSSI, Y)** \\ \hline RSSI & 1.00000 & 1.00000 & 1.00000 \\ Temperature 1 & 0.16330 & \(-\)0.18181 & \(-\)0.13946 \\ Pressure & 0.06568 & 0.16756 & 0.12736 \\ Temperature 2 & 0.15377 & \(-\)0.18674 & \(-\)0.14321 \\ Humidity & \(-\)0.02592 & \(-\)0.19099 & \(-\)0.15497 \\ Temperature 3 & 0.17732 & \(-\)0.18033 & \(-\)0.13849 \\ Wind speed & \(-\)0.01588 & 0.14626 & 0.11344 \\ Wind voltage & \(-\)0.01594 & 0.14622 & 0.11343 \\ Particles in the air & 0.17781 & \(-\)0.01625 & \(-\)0.01391 \\ Visibility & 0.24912 & \(-\)0.12074 & \(-\)0.10815 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of correlation of RSSI parameter with individual parameters of climatic conditions. The difference between Tables 5 and 6 is the fact that Table 5 shows the average values of temperatures and wind speeds from several sensors. Although it might seem that the prediction efficiency should not change, as we only averaged similar values from several sensors, from Table 6 we see that this is not the case. It could be caused by the different location of the sensors, their measurement deviation, or the specific properties of the sensors. However, averaging further reduces the time required to train algorithms. The actual values of the RSSI parameter are shown in Figure 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training with Data** & **Length of the Decision** & **Training Process** & **Decision Tree** & **AdaBoost Prediction** \\
**from Each** & **Tree Training Process** & **Length (AdaBoost)** & **Prediction Efficiency** & **Efficiency** \\ \hline
1 min & 0.499773 s & 0:53 min & 78\% & 86\% \\
2 min & 0.228143 s & 0:24 min & 69\% & 84\% \\
3 min & 0.147259 s & 0:15 min & 71\% & 85\% \\
5 min & 0.090056 s & 0:08 min & 58\% & 73\% \\
10 min & 0.044001 s & 0:03 min & 0.09\% & 62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of individual algorithms (120,000 records, n_estimators = 150, training ratio 80:20)—data used: temperature average; pressure; humidity; wind speed average; particle concentration, visibility. Figure 9: Actual values of the RSSI parameter for the entire data set (120,000 records). At the beginning, we started testing the decision tree method. If we collected a small amount of data (17 November 2020 16:40 to 19:40) and all the measured values, the results were not optimal. The prediction was not of high quality and the value of SqRMSE itself was at the level of 0.393997 dBm. Therefore, we also tried the decision tree method on the same file using the AdaBoost Regressor. In this case, the results were slightly better. The SqRMSE value was 0.384952 dBm. As can be seen in Figure 10, the prediction using decision tree and decision tree using AdaBoost Regressor was practically the same. In the next case, we wanted to test the accuracy of the prediction from a given data set, from which we selected values from every second and every third minute. These results did not turn out well at all, as the SqRMSE values for every second minute were 0.739108 dBm and for every third minute were 0.969592 dBm.