# 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
#### 4.6.1 Maximum Temperature Prediction  
Table 17 in Appendix E includes sample maximum temperature values as measured, predicted by the MLP, and predicted by the weather service. Table 9 presents the quantitative values of the MLP and two other predictive model effectiveness measures as well as the weather service in forecasting the maximum temperature. Figure 3 illustrates a comparison of the effectiveness of the MLP model and the weather service in forecasting the maximum temperature. The MAE was equal to 2.0201 for the MLP and this is lower by 0.3252 compared with the MAE calculated for the weather service, which was equal to 2.3453. This shows that the MLP was more accurate compared with the weather service in terms of this measure of prediction accuracy. The MSE calculated for the MLP was lower than MSE calculated \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MLP**} & \multicolumn{3}{c}{**LSTM**} & \multicolumn{3}{c}{**SVR**} & **Service** \\ \hline  & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} & **Test** & \\ \hline  & \multicolumn{2}{c}{**Mean**} & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & & \\ \hline MAE & 2.0415 & 0.0063 & 1.8236 & 0.0143 & 2.0196 & 0.0374 & 1.7475 & 0.0633 & 1.5551 & 1.2230 & 1.0505 \\ MSE & 15.6428 & 0.0512 & 10.5480 & 0.0374 & 16.2313 & 0.1525 & 10.5223 & 0.0419 & 17.8979 & 11.5570 & 10.0980 \\ R\({}^{2}\) & 0.5371 & 0.0015 & 0.5184 & 0.0017 & 0.5197 & 0.0045 & 0.5196 & 0.0019 & 0.4703 & 0.4724 & 0.5390 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of the effectiveness of the MLP model, two other models, and the weather service in forecasting the daily precipitation sum. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MLP**} & \multicolumn{3}{c}{**LSTM**} & \multicolumn{3}{c}{**SVR**} & **Service** \\ \hline  & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} & **Test** & \\ \hline  & \multicolumn{2}{c}{**Mean**} & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & & \\ \hline MAE & 3.7830 & 0.0087 & 4.0537 & 0.0094 & 4.2301 & 0.0564 & 4.0602 & 0.0138 & 3.1029 & 4.2030 & 4.4267 \\ MSE & 24.1300 & 0.0553 & 29.1347 & 0.1087 & 29.4683 & 0.7534 & 29.3869 & 0.1467 & 18.6167 & 32.2005 & 37.4616 \\ R\({}^{2}\) & 0.8427 & 0.0004 & 0.8327 & 0.0006 & 0.8079 & 0.0049 & 0.8312 & 0.0008 & 0.8787 & 0.8151 & 0.7849 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the effectiveness of the MLP model, two other models, and the weather service in forecasting the minimum temperature. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MLP**} & \multicolumn{3}{c}{**LSTM**} & \multicolumn{3}{c}{**SVR**} & **Service** \\ \hline  & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} & **Test** & \\ \hline  & \multicolumn{2}{c}{**Mean**} & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & & \\ \hline MAE & 0.9097 & 0.0181 & 0.8795 & 0.0119 & 0.9354 & 0.0048 & 0.8551 & 0.0035 & 0.8042 & 0.8765 & 1.2526 \\ MSE & 1.3807 & 0.0559 & 1.3453 & 0.0276 & 1.4480 & 0.0135 & 1.2764 & 0.0041 & 1.1987 & 1.3334 & 2.6179 \\ R\({}^{2}\) & 0.6719 & 0.0133 & 0.6029 & 0.0081 & 0.6559 & 0.0032 & 0.6232 & 0.0012 & 0.7152 & 0.6064 & 0.2272 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of the effectiveness of the MLP model, two other models, and the weather service in forecasting the mean daily wind speed.