# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## Appendix D Sensitivity Analysis for Each Input Parameter Of Model  
The results of the sensitivity analysis for the maximum temperature prediction with the values of MSE ranked in descending order for the training and testing datasets arelisted in Table 14. The results for the other predicted weather conditions are included in Tables 15-16. In the case of the prediction of the maximum temperature, the training and testing MLP model without mean temperature 1 day ago, training without the sum of precipitation 1 day ago, general cloud cover 1 day ago, mean atmospheric pressure 1 day ago, and testing without mean atmospheric pressure 2 days ago demonstrated a remarkable increase in the error values. In the event of the prediction of the minimum temperature, the training and testing MLP without minimum temperature 1 day ago, the sum of precipitation 1 day ago, general cloud cover 1 day ago, relative humidity 1 day ago, and testing without mean temperature 1 day ago caused a rise in the error values. When the mean atmospheric pressure was predicted, the training and testing MLP without mean atmospheric pressure 2 days ago, training without dew duration 3 days ago, mean temperature 1 day ago, minimum temperature 2 days ago, testing without mean atmospheric pressure 1 day ago, minimum temperature 1 day ago, and mean temperature 3 days ago resulted in the growth of the error values. For the wind speed prediction, the training and testing MLP without wind speed 1 day ago, training without the sum of precipitation 2 days ago, general cloud cover 2 days ago, dew duration 1 day ago, testing without wind speed 3 days ago, and mean temperature 2 days ago demonstrated a remarkable increase in the error values. In the case of the prediction of the daily sum of precipitation, the training MLP without mean atmospheric pressure 1 day ago, dew duration 2 days ago and 1 day ago, the sum of precipitation 1 day ago, testing without mean temperature 1 day ago, and maximum temperature 1 day ago caused a rise in the error values. \begin{table}
\begin{tabular}{l c c} \hline
**Input Out** & **Training MSE** & **Testing MSE** \\ \hline Mean temp. 1 day ago & 6.1293 & 7.1936 \\ Sum of precip 1 day ago & 6.1096 & 6.6900 \\ Cloud cover 1 day ago & 5.6495 & 6.4668 \\ Mean pressure 1 day ago & 5.6224 & 6.6076 \\ Mean pressure 3 days ago & 5.5493 & 6.6871 \\ Min. temp. 3 days ago & 5.4739 & 6.4487 \\ Min. temp. 1 day ago & 5.4714 & 6.5110 \\ Wind speed 2 days ago & 5.4314 & 6.4600 \\ Max. temp. 2 days ago & 5.3495 & 6.4757 \\ Dew duration 2 days ago & 5.3297 & 6.4460 \\ Cloud cover 3 days ago & 5.3293 & 6.4904 \\ Wind speed 1 day ago & 5.3034 & 6.5390 \\ Relative humidity 1 day ago & 5.2536 & 6.5292 \\ Max. temp. 3 days ago & 5.2047 & 6.4087 \\ Mean temp. 3 days ago & 5.1212 & 6.4370 \\ Mean temp. 2 days ago & 5.1123 & 6.6664 \\ Wind speed 3 days ago & 5.1070 & 6.4721 \\ Min. temp. 2 days ago & 5.1020 & 6.7268 \\ Cloud cover 2 days ago & 5.0996 & 6.6013 \\ Max. temp. 1 day ago & 5.0857 & 6.5947 \\ Dew duration 1 day ago & 5.0502 & 6.5090 \\ Relative humidity 2 days ago & 5.0488 & 6.5872 \\ Min. temp. at ground level 3 days ago & 5.0239 & 6.5584 \\ Min. temp. at ground level 1 day ago & 4.9977 & 6.7814 \\ Min. temp. at ground level 2 days ago & 4.8545 & 6.4719 \\ \hline \end{tabular}
\end{table} 
Table 14: The results of the sensitivity analysis for the prediction of the maximum daily temperature.