# 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
We used the MAE, MSE, and R\({}^{2}\) score as a measure of the accuracy of the MLP model prediction and weather service, calculated for the test data as these are frequently used and proven measures found in the literature for the prediction of weather parameters [32; 35; 36; 37]. The smaller the values of MAE and MSE were, the more accurate the model was in predicting the tested parameters. High values of the R\({}^{2}\) score, close to 1, were evidence of the high efficiency of the tested model. The tables with values of the weather conditions measured according to the IMGW, predicted by the MLP, and predicted by the service, include sample data from 1 January 2018 to 5 January 2018. This demonstrated the values of the parameters that we are investigating; however, the comparison was made for data from 1 January 2018 to 31 December 2020, and thus our study covers the whole 2018, 2019, and 2020 years. To compare the effectiveness of the MLP model and the weather service, quantitative model effectiveness models, such as MAE, MSE, and the determination factor R\({}^{2}\) score, were used. Charts of the parameters measured and projected by the MLP model and weather service were compared in terms of the visual differences in the graphs. The tables also contain the prediction results of the studied weather conditions obtained using two other ML predictive models. One of the benchmark models was the LSTM neural network with a complexity of architecture and parameters comparable to the MLP model used in the study. The second model used for comparison was SVR. These models were chosen because they are often used in weather prediction studies, as mentioned in our literature review. The ANN models were trained and tested 10 times; therefore, in the tables, the mean and standard deviation values of the results are provided. The results of the evaluation models on the trainnig and testing dataset are presented in Tables 9-13. Based on the small differences between the accuracies for the training and test data, there was no overtraining of the models or over-fitting to the training data. In the next step, the prediction results of the tested models and the weather service were compared for the test data. \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**} & **Train** & **Test** & \\ \hline  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & & \\ \hline MAE & 1.9842 & 0.0095 & 2.0201 & 0.0212 & 2.0871 & 0.0339 & 2.0466 & 0.0180 & 1.6747 & 2.0792 & 2.3453 \\ MSE & 6.3276 & 0.0646 & 6.5448 & 0.0716 & 6.9771 & 0.2054 & 6.7315 & 0.1205 & 4.9096 & 7.0436 & 9.2178 \\ R\({}^{2}\) & 0.9570 & 0.0004 & 0.9573 & 0.0005 & 0.9525 & 0.0014 & 0.9561 & 0.0008 & 0.9666 & 0.9540 & 0.9398 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the effectiveness of the MLP model, two other models, and the weather service in forecasting the maximum temperature.