# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 4 Results
### Results of Data Preprocessing  
Figures A1-A4 in Appendix C display histograms of the input data before and after the data preprocessing. The shape of the histograms reflects the nature of the data resulting from the specific climate of the city of Szczecin. The presentation of histograms before and after data normalization next to each other allows for a clear visualization of the effect of data normalization on the the histogram shape change. The histogram of the maximum daily temperature in Figure A1a demonstrated an interesting characteristic. This plot shows that the data was multimodal, which may be due to two different sets of environmental circumstances.  
The maximum temperature swings in the area of the city of Szczecin can be quite extreme, particularly between seasons. The histogram for the minimum daily temperature shown in Figure A1c after normalization obtained a shape more similar to a histogram with a normal distribution as shown in Figure A1d. The histogram representing the mean daily temperature seen in Figure A2a indicates that this data was somewhat multimodal. Normalization eliminated this effect as can be seen in Figure A2b.  
In the case of the minimum daily temperature at ground level, a slight shift toward the high values observed in the histogram in Figure A2c was canceled out after applying normalization, as shown in Figure A2d. For the daily sum of the precipitation, the outliers visible in the histogram in Figure A2e are easy to explain because dry days (days without precipitation) were more frequent. Outliers are also visible for the data in the histogram for the daily dew duration in Figure A3a.  
Szczecin's climate is characterized by high cloudiness, humidity, and fog, which is reflected in the histograms presenting the input data representing the weather parameters [72]. The histogram of the mean daily general cloud cover in Figure A3c shows that  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Predicted Condition** & **Preprocessing Method** & **MAE** & **MSE** & \(\mathbf{R}^{2}\) \\ \hline \multirow{2}{*}{Maximum temperature} & IQR & 2.0184 & 6.7045 & 0.9562 \\  & Yeo–Johnson trans. & 2.0136 & 6.5105 & 0.9575 \\ \hline \multirow{2}{*}{Minimum temperature} & IQR & 1.8037 & 4.9932 & 0.9397 \\  & Yeo–Johnson trans. & 1.7524 & 4.7432 & 0.9427 \\ \hline \multirow{2}{*}{Atmospheric pressure} & IQR & 4.0796 & 29.7486 & 0.8292 \\  & Yeo–Johnson trans. & 4.0458 & 29.3731 & 0.8313 \\ \hline \multirow{2}{*}{Wind} & IQR & 0.9159 & 1.4239 & 0.5797 \\  & Yeo–Johnson trans. & 0.8951 & 1.3627 & 0.5977 \\ \hline \multirow{2}{*}{Precip.} & IQR & 1.8417 & 10.5228 & 0.5196 \\  & Yeo–Johnson trans. & 1.2230 & 11.4712 & 0.4763 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the weather condition prediction accuracy using the Yeo–Johnson transformation and IQR in data preprocessing.  
there were more samples with high values as a result of the large number of days per year with high cloud cover due to the specific climate of the city of Szczecin. In the case of the histogram for the data representing the mean daily humidity as seen in Figure 19a, normalization reduced the shift toward high values as can be seen in Figure 19b.  
The histogram for the data representing the mean daily wind speed as shown in Figure 19e presents that more samples had low values due to fewer days with strong winds. After normalization, this effect was significantly reduced, as shown in Figure 19f. The histogram representing the data for the mean daily atmospheric pressure in Figure 19c shows that the data exhibited a normal distribution because the histogram has a characteristic bell shape. The mean value is located in the central part of the histogram.