## 6 Conclusions  
In this study, a DNN-based deep learning model was trained for predicting the ore production and crusher utilization of a truck haulage system in an underground mine. An underground limestone mine was selected as the study area, and the DNN model input nodes were configured to reflect the truck haulage system of the study area. The DNN model was trained by setting various hidden layer configurations, and the coefficient of determination and MAPE for the training and test data were calculated to evaluate the model prediction accuracy. The DNN model was trained considering the haulage work operating conditions in the study area, and it was used to predict the ore production and crusher utilization over 10 days, starting on 1 November 2018. The predicted results were compared with the actual observed results. The prediction accuracy for the ore production was the highest when the number of hidden layers of the DNN model was set to four and the number of hidden layer nodes was set to 40. In this case, the coefficient of determination for the test data was approximately 0.99, while the MAPE was approximately 2.80%. The prediction accuracy for the crusher utilization was the highest when the number of hidden layers of the DNN model was set to four and the number of hidden layer nodes was set to 40. In this case, the coefficient of determination for the test data was approximately 0.99, while the MAPE was approximately 2.49%. The predicted ore production and crusher utilization for the 9 days of haulage work excluding 7 November tended to match the observed values. On 7 November, the trucks used a haulage path longer than that normally used to haul ore, and as a result, the predicted and observed values did not match. The RMSE of the ore prediction for the 9 days excluding 7 November was approximately 6.92 tons, while that for the crusher utilization was approximately 0.04%. The DNN model input nodes were set considering the operating conditions of the ore haulage work performed on 5 November 2018. The number of trucks and truck dispatch interval were set to various values, and the changes in the ore production and crusher utilization were predicted. The results demonstrated that a daily ore production target of 3000 tons can be satisfied if the number of ore haulage trucks is set to four and the truck dispatch interval is set to 1 to 2 min. It was confirmed that the DNN model-based ore production and crusher utilization prediction methods presented in this study can be used for short-term or long-term mine haulage system design and prediction. We trained the DNN models for ore production and crusher utilization prediction and tested them on the first 10 days of the following month. The DNN models predicted the target values accurately for 9 days; however, the models could not accurately predict the target value on the day the truck took  
Figure 13: Results of analyzing variations in ore production and crusher utilization according to: (a) number of trucks and (b) truck dispatch interval (min). a different path than usual. This may mean that the DNN models were successful because there was not a lot of variation between the routines of each day. The DNN models may only be memorizing the outputs given the inputs (over-fitting), which would make them very unreliable. To solve this problem, the models must be further trained such that the varying conditions of the mine can be taken into account. Alternatively, by training the model on a longer time span, all possible paths can be used as input parameters, and over-fitting can be avoided. Therefore, further studies are required in order to select a reasonable DNN model update cycle and determine the training data acquisition time span to consider various types of changing conditions in mines.