# 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  
Table 16 in Appendix E includes examples of the daily precipitation values as measured, predicted by the MLP, and forecast by the weather service. Figure 7 illustrates a comparison of the effectiveness of the MLP model and the weather service forecasting the daily precipitation sum. The effectiveness of the MLP model and the weather service forecasting the daily sum of the precipitation was lower than with the other weather parameters. Table 13 presents quantitative measures of the effectiveness of the MLP, two other predictive models, and the weather service in forecasting the daily precipitation sum. For the prediction of the daily precipitation, the values provided by the weather service were more accurate than the values predicted by the MLP model and the other models. The value of MAE was 0.7731 higher for the MLP, and the value of the MSE was 0.45 higher for the MLP compared with for the weather service. The value of the R\({}^{2}\) score was lower by 0.0206 for the MLP when compared with the weather service. The prediction accuracy of the daily precipitation was comparable for the MLP and LSTM models, while it was lower for the SVR model. The effectiveness of all the predictive models was lower for the sum of the daily precipitation than it was for the other weather parameters analyzed in this survey. Figure 8 shows a bar graph comparing the values of the prediction effectiveness measure R\({}^{2}\) score for all tested weather parameters for the MLP and weather service. Figure 7: Comparison between the observed values and those predicted by the MLP and the weather service mean precipitation. For the MLP, the highest predictive performance was observed at the maximum (0.9573) and minimum (0.9423) temperatures. This was followed by atmospheric pressure, for which the R\({}^{2}\) score was 0.8327, followed by wind speed (0.6029), and lastly by precipitation (0.5184). For the prediction of the daily maximum temperature, the predictions of the compared models and the weather service had high accuracy and were comparable; however, all models achieved higher accuracy compared with the weather service. For the daily minimum temperature, the prediction accuracy of the benchmarked models was high and comparable. They were further superior to the weather service than was observed with the maximum temperature. In the case of prediction studies of the daily mean atmospheric pressure, the accuracy of the tested models was lower than for the temperatures. The MLP and LSTM models showed the highest prediction accuracy, and the prediction accuracy by SVR was slightly lower. However, all the models tested provided a more accurate prediction than the weather service did. The greatest advantage of the predictive models over the weather service was observed for the daily average wind speed. The LSTM model obtained the highest accuracy in this case, followed by SVR, and the accuracy of the MLP was minimally lower in comparison. The daily precipitation is the weather condition that was the most difficult to accurately predict for the models used in this study. It is the only weather condition in this study for which the weather service forecast was more accurate than the other predictive models. MLP and LSTM were superior to the SVR model in terms of the daily precipitation prediction accuracy. The output data histograms of the weather conditions predicted with the MLP model are presented in Appendix C in Figure A5. The observed shape of the histograms for the output data reflect the shapes and nature of the input data before normalization. The maximum daily temperature shows a multimodal distribution that was also observed for the input data representing this feature. In the case of the mean daily wind speed, we observed that more samples were shifted toward low values on the histogram, which was similar to the input data. The mean daily atmospheric pressure demonstrated a normal distribution, which also reflects the nature of the input data for this parameter.