# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 4 Results
### Experimental Evaluation of Trained DNN Models
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No. of Hidden Layers** & **No. of Hidden Layer Nodes** & **Statistics of MAPE** & \multicolumn{4}{c}{**Round of 5-Fold Cross Validation**} & \multicolumn{1}{c}{**Total Mean**} \\ \hline \hline \multirow{4}{*}{3} & \multirow{2}{*}{30} & Mean & 6.06 & 6.09 & 6.22 & 6.13 & 5.90 & 6.08 \\  & & STD & 0.26 & 0.47 & 0.43 & 0.17 & 0.19 & 0.30 \\ \cline{2-8}  & & \multirow{2}{*}{40} & Mean & 5.54 & 5.43 & 5.77 & 5.37 & 5.66 & 5.55 \\  & & STD & 0.24 & 0.21 & 0.38 & 0.38 & 0.10 & 0.26 \\ \cline{2-8}  & & \multirow{2}{*}{50} & Mean & 4.97 & 5.14 & 5.30 & 5.04 & 5.12 & 5.11 \\  & & STD & 0.13 & 0.36 & 0.58 & 0.32 & 0.21 & 0.32 \\ \hline \hline \multirow{4}{*}{4} & \multirow{2}{*}{30} & Mean & 5.66 & 5.87 & 5.81 & 5.66 & 6.02 & 5.81 \\  & & STD & 0.22 & 0.17 & 0.32 & 0.25 & 0.38 & 0.27 \\ \cline{2-8}  & & \multirow{2}{*}{40} & Mean & 5.09 & 5.00 & 4.90 & 5.06 & 5.12 & 5.04 \\  & & STD & 0.34 & 0.18 & 0.30 & 0.18 & 0.41 & 0.28 \\ \cline{2-8}  & & \multirow{2}{*}{50} & Mean & 4.98 & 4.77 & 4.64 & 4.75 & 4.78 & 4.78 \\  & & STD & 0.21 & 0.24 & 0.17 & 0.28 & 0.15 & 0.21 \\ \hline \hline \multirow{4}{*}{5} & \multirow{2}{*}{30} & Mean & 5.45 & 5.53 & 5.73 & 5.54 & 5.39 & 5.53 \\  & & STD & 0.43 & 0.36 & 0.26 & 0.39 & 0.30 & 0.35 \\ \cline{2-8}  & & \multirow{2}{*}{40} & Mean & 5.36 & 5.14 & 4.87 & 4.77 & 5.22 & 5.07 \\  & & STD & 0.09 & 0.39 & 0.29 & 0.20 & 0.54 & 0.30 \\ \cline{2-8}  & & \multirow{2}{*}{50} & Mean & 4.75 & 5.02 & 4.95 & 4.87 & 4.74 & 4.87 \\  & & STD & 0.14 & 0.18 & 0.25 & 0.49 & 0.20 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical analysis results of mean absolute percentage error (MAPE) of DNN models for predicting morning ore production according to the number of rounds of 5-fold cross validation. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**DNN Models** & \multicolumn{4}{c}{**(3, 30)**} & \multicolumn{4}{c}{**(3, 40)**} & \multicolumn{4}{c}{**(3, 50)**} & \multicolumn{4}{c}{**(4, 30)**} & \multicolumn{4}{c}{**(4, 40)**} & \multicolumn{4}{c}{**(4, 50)**} & \multicolumn{4}{c}{**(5, 30)**} & \multicolumn{4}{c}{**(5, 50)**} \\
**No. of Hidden Layers,** & \multicolumn{4}{c}{**(3, 40)**} & \multicolumn{4}{c}{**(3, 40)**} & \multicolumn{4}{c}{**(3, 50)**} & \multicolumn{4}{c}{**(4, 30)**} & \multicolumn{4}{c}{**(4, 40)**} & \multicolumn{4}{c}{**(4, 50)**} & \multicolumn{4}{c}{**(5, 30)**} & \multicolumn{4}{c}{**(5, 50)**} \\
**No. of Hidden Layer Nodes** & \multicolumn{4}{c}{} & & & & & & & & \\ \hline \hline \multirow{4}{*}{(3, 30)} & \multirow{2}{*}{\(p<0.05\)} & \multirow{4}{*}{NS} & \multirow{2}{*}{\(p<0.05\)} & \multirow{4}{*}{NS} & \multirow{2}{*}{\(p<0.05\)} & \multirow{4}{*}{\(p<0.05\)} & \multirow{4}{*}{NS} & \multirow{2}{*}{\(p<0.05\)} & \multirow{4}{*}{\(p<0.05\)} & \multirow{4}{*}{NS} \\
**(4, 30)** & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \multirow{2}{*}{\(p<0.05\)} & \multirow{2}{*}{NS} & \\
**(4, 40)** & \multirow{2}{*}{\(p<0.05\)} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \\
**(5, 40)** & \multirow{2}{*}{\(p<0.05\)} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \multirow{2}{*}{\(p<0.05\)} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \\
**(5, 50)** & \multirow{2}{*}{\(p<0.05\)} & \multirow{2}{*}{NS} & \multirow{2}{*}{NS} & \multirow{2}{Table 8 shows statistical analysis results of MAPE of DNN models for prediction afternoon ore production, in accordance with the number of rounds of 5-fold cross validation. It was observed that the prediction error of afternoon ore production was lowest when the number of hidden layers and number of corresponding nodes were (5, 50), (4, 50), and (3, 50), respectively. As observed in Table 9, there was no statistically significant differences in predictive performances among the three DNN models.