# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
###### Abstract
Deep learning models are classified into several types, such as a deep neural network (DNN) [45; 46], convolutional neural network (CNN) [47; 48], and recurrent neural network (RNN) [49; 50], depending on the input-data type and data-processing method employed. Recently, various investigations have been performed to incorporate deep learning techniques into mining-industry applications. Models that can detect anomalies [51; 52; 53], analyze deposit potential [54; 55; 56; 57], predict fuel consumption by trucks [58], and recognize cutting patterns of coal-mining shearers [59] have been developed. Additionally, several risk-prediction systems employing deep learning techniques have been developed to prevent occurrence of mining accidents [60; 61]. Other deep learning-based systems that have been recently developed include a visual object-detection system that can classify workers and heavy mining vehicles in open-pit mines [62], stability-evaluation system for gate roadways in a long wall-mining environment [63], and prediction system for health risks caused by whole body vibration of mining-truck drivers [64]. [PERSON] and [PERSON] [65] developed a DNN model to predict ore production and crusher utilization of a truck-haulage system using equipment-tracking data obtained using a wireless network system installed in an underground mine. However, no algorithm capable of learning truck-haulage-system characteristics and predicting ore production using equipment-tracking data has yet been developed for use in open-pit mining applications. Predicting ore production through deep learning using equipment-tracking data affords several advantages. It aids comprehension of the operating patterns of haulage equipment and facilitates ore-production prediction exclusively using data obtained from mining sites without developing algorithms based on prior knowledge. Even in cases involving changes in the haulage plan, ore-production prediction can be realized by simply updating the prediction model with the latest datasets. Moreover, prediction of the latest ore produce can be realized by providing data obtained in real time as input to the prediction model. Furthermore, deep learning methods can be used throughout the lifetime of mines by continuously updating their underlying prediction model. In this study, an open-pit limestone mine installed with an ICT-based mine-safety-management system was used as the investigation location. Two DNN models were designed to predict morning and afternoon ore productions, respectively, with the input-layer nodes of each model configured with the truck-haulage operating conditions and haulage-operation time as variables. DNN-model training data were generated by processing packet data collected over the two-month period December 2018 to January 2019. The two models were trained by varying the number of hidden layers and their corresponding nodes, and their prediction accuracy was evaluated by calculating the determination coefficient and mean absolute percentage error (MAPE) for the training and validation data. By using the DNN model with the lowest prediction error, morning and afternoon ore productions on five days of the second week of February 2019 were predicted and compared against observed values.