## 6 Discussion  
The accuracies of the NOx emissions prediction achieved in the training, validation, and testing datasets are given in Table 5. To check the robustness of the developed ANN-based model, different statistical metrics are used.  
The cumulative error of the NOx prediction over time is shown in Figure 16 for all data. The coefficient of determination seems high--up to 0.86757--and the total deviation of NOx emissions is moderate during half of the working shift (about 3 h).  
Using the ANN-based MLP model for the prediction of NOx emissions allows the estimation of the environmental impacts of LHD vehicles working in the underground mine and equipped with the monitoring system. Since not every LHD has a NOx sensor, the results obtained can easily be implemented on a large number of working machines.  
The main problem in NOx prediction is to account for the transient modes of LHD bucket filling, acceleration, and deceleration when the engine is subjected to maximum loading and fuel combustion. However, following the analysed dataset, which represents many working cycles, the duration of several outliers in NOx emission is very short (several seconds) compared to the entire time; therefore, the amount of NOx is less than 1%. Therefore, before ANN training, those samples can be rejected, which greatly improves prediction accuracy (up to 85%). The criterion for outlier rejection from the dataset can be a condition of NOx > 3 \(\times\) STD (standard deviation), i.e., beyond the Gaussian distribution range.  
For longer distances of delivery, i.e., fewer LHD cycles per working shift, the accuracy of predictions is expected to be higher due to more-stable modes of diesel engine loading with fewer transient periods. Pieces of blasted material with bigger sizes create a greater load on the engine when the LHD bucket penetrates the hill; hence, heterogeneous material will reduce the accuracy of NOx prediction.  
Figure 16: Cumulative NOx emissions: original measured data and predicted (**a**); error of MLP prediction (**b**).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Errors** & **Training** & **Validation** & **Testing** & **All Data** & **Units** \\ \hline Mean Average Error (MAE) & 12.8827 & 13.6737 & 13.6737 & 13.1139 & ppm \\ Mean Squared Error (MSE) & 290.6143 & 317.3026 & 330.2508 & 300.558 & — \\ Root Mean Squared Error (RMSE) & 17.0474 & 17.813 & 18.1728 & 17.3366 & ppm \\ Coefficient of determination (\(R^{2}\)) & 0.86757 & 0.85498 & 0.84919 & 0.86293 & — \\ \hline \hline \end{tabular}
\end{table}
Table 5: Errors of NOx emission prediction for datasets.  
Future research is directed toward improving the prediction of NOx emissions in transient modes of operation of LHD vehicles (e.g., excavation of bulk material and reverse motion). This ANN-based \"soft-sensor\" can be utilized in ventilation power demand estimation in certain geological conditions of underground mining, which accounts for material granulation after blasting, road inclination, and distance of delivery to dumping points. In addition, trend analysis in NOx emission of certain LHD vehicles can be used for the assessment of individual operator driving qualifications. Further, trends of NOx emissions in certain LHD vehicles over all operators can demonstrate malfunctions in fuel injection or after-treatment systems as well as supplied fuel quality.