## 4 Results
From Table 3, at the Pearson correlation coefficient, we see that the intensity of the received RSSI signal is most affected by visibility, particle concentration, and temperature, which are closely related to rain, snow, or fog. For this reason, we tried to train individual prediction models using only these specific parameters or by averaging individual temperatures, wind speeds, and strengths. We were mainly interested in reducing the required computing power, as with the gradual growth of the database of input values, small details will fundamentally affect the required performance. That is why we also started experimenting with using data not from every minute but every second minute, third, fifth, or tenth minute. We could afford it, as the weather usually does not change from minute to minute, but is always a longer process. In our database with 120,000 records, we saw the use of every second or third row as the most suitable, when the deviations in the prediction of individual algorithms were still in a low range. As the first machine learning model, we decided to try decision trees. Decision trees are based on the principle of a hierarchical multi-stage binary decision-making system, in which the fulfillment or non-fulfillment of individual decision-making conditions is gradually evaluated until we reach an acceptable solution. Furthermore, it was necessary to divide the input data set into algorithm training data and test data. We chose a ratio of 80% training data and 20% testing. We also conducted experiments with a ratio of 70:30, but the results of the prediction success were slightly weaker. To optimize the complexity of the  
Figure 8: Example of formatting input data in a CSV file. training algorithm and the amount of data generated during training, we decided to first try to calculate the average from the data obtained from three temperature sensors, as well as two wind speed sensors, and then support only these averaged values in the training algorithm. In the next step, we performed an arithmetic mean for the temperature data, where we added the values from all three sensors and divided them by three, and for the wind speed data, where we added the data from both sensors and divided them by two. It is known that decision trees improved using the AdaBoost algorithm are characterized by higher accuracy than pure decision trees. Using AdaBoost regression, we managed to achieve a success rate of up to 92%, which is 11% more than with the common decision tree algorithm (sample of 120,000 records, data from every minute). However, the complexity of the computational algorithm is many times higher with AdaBoost regression than with decision trees. With 120,000 records, the training process took up to 1 min 17 s compared to 0.667425 s for decision trees. We have constructed the following tables and graphs based on several outputs from various data configurations and programs. Tables 4-6 compare the success and length of the decision tree and AdaBoost algorithm training process for data from every 1 st, 2 nd, 3 rd, 5 th, and 10 th minute. We see that even when using data from every second or third minute, the prediction efficiency still reaches high values. The difference between Tables 4 and 5 lies in the different value of the n_estimators parameter of the AdaBoostRegressor function. Table 4 shows the values for the value n_estimators = 300, while Table 5 shows the values for the value n_estimators = 150. As we can see from the values, the effectiveness of the AdaBoost prediction is practically unchanged, while the time required for training has doubled in any case. As a result, we have determined that limiting this value to 150 will not adversely affect the results. At values lower than 150, there was already a gradual decrease in the effectiveness of the prediction, for example, with the value of n_estimators = 130 and data from each minute, we achieved a success rate of only 89% compared to the original 92%.