# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 4 Results
### Comparison of the Results Obtained by MLP, Two Other ML Models, and the Weather Service
#### 4.6.5 Daily Precipitation Prediction
For the daily sum of the precipitation, more samples had low values, similar to what was observed for the input data representing this parameter. To investigate the reason that the predictions for target variables, such as the atmospheric pressure, wind speed, and daily precipitation sum, were less effective than the  
Figure 8: Comparison of the values of the R\({}^{2}\) score for all predicted parameters obtained by the MLP and weather service. predictions for temperature, we checked whether their values in the time series depend on the features based on which they were predicted or whether they are rather random. To check the dependence of the target weather variables on the explanatory variables (i.e., the traits), the strength of the correlation between the forecast weather parameters and the traits in the training dataset was examined using the r-Pearson correlation coefficient. We examined the number of characteristics for which the absolute value of the Pearson's coefficient of correlation with the predicted parameter was equal to or greater than 0.6, as this value is considered to be a strong correlation threshold. Tables 14-16 includes characteristics that reached an r-Pearson correlation coefficient greater than or equal to 0.6 for the eight weather parameters that were training features. In the column next to the names of these features, the values of the r-Pearson correlation coefficient are presented. For predicted parameters, such as the wind and precip, there were no training characteristics where the correlation was greater than 0.6. The above analysis demonstrated that parameters, such as the maximum temperature and minimum temperature, had a strong correlation with many other parameters that were features of the training dataset. For these variables, the best prediction results were \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Minimum Daily Temperature** \\ \hline The minimum daily temperature on the ground 3 days ago & 0.7665 \\ Minimum daily temperature 3 days ago & 0.7954 \\ The minimum daily temperature on the ground 2 days ago & 0.8028 \\ Maximum daily temperature 3 days ago & 0.8292 \\ Minimum daily temperature 2 days ago & 0.8307 \\ Mean daily temperature 3 days ago & 0.8461 \\ Maximum daily temperature 2 days ago & 0.8551 \\ The minimum daily temperature on the ground 1 day ago & 0.8674 \\ Mean daily temperature 2 days ago & 0.8791 \\ Maximum daily temperature 1 day ago & 0.8867 \\ Minimum daily temperature 1 day ago & 0.8873 \\ Mean daily temperature 1 day ago & 0.9279 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: The parameters with the strongest correlation to the predicted minimum temperature. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Mean Daily Atmospheric Pressure** \\ \hline Mean daily atmospheric pressure 1 day ago & 0.8014 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The parameters with the strongest correlation to the predicted mean daily atmospheric pressure. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Maximum Daily Temperature** \\ \hline The minimum daily temperature on the ground 3 days ago & 0.7492 \\ The minimum daily temperature on the ground 2 days ago & 0.7692 \\ Minimum daily temperature 3 days ago & 0.7894 \\ The minimum daily temperature on the ground 1 day ago & 0.7937 \\ Minimum daily temperature 2 days ago & 0.8109 \\ Minimum daily temperature 1 day ago & 0.8396 \\ Mean daily temperature 3 days ago & 0.8691 \\ Maximum daily temperature 3 days ago & 0.8770 \\ Mean daily temperature 2 days ago & 0.8969 \\ Maximum daily temperature 2 days ago & 0.9029 \\ Mean daily temperature 1 day ago & 0.9417 \\ Maximum daily temperature 1 day ago & 0.9493 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: The parametersobtained using the MLP model. The results of the above analysis were different for the mean daily atmospheric pressure. In the case of this variable, there was a strong correlation with only one parameter that was a feature from the training dataset.