Deep neural network models for improving truck productivity prediction in openpit mines.

Accurate prediction of truck productivity plays a pivotal role in improving the efficiency and profitability of openpit mining operations. This paper proposes a deep neural network (DNN) model to overcome the challenge of predicting truck productivity in openpit mines. The prediction model was built using eight variables and was optimized by considering different train-test split ratios, numbers of hidden layers and neurons, and activation functions. The proposed model's performance was evaluated using various metrics and compared with other commonly used machine learning algorithms. The results showed that the proposed model outperformed traditional machine learning models by achieving higher prediction accuracy. Moreover, a single-variable sensitivity analysis showed that haul distance is the most influential variable for predicting truck productivity. This study marks a pioneering effort in employing DNN to predict truck productivity in openpit mining, signifying a notable advancement in the field. [ABSTRACT FROM AUTHOR]