XGBoost and LSTM predict next-shift hauling payload with median absolute errors around 14 to 15 percent, improving to 8.4 percent when scheduled-truck counts are added, though the unique contribution of simulated breakdown data is not demonstrated.
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Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data
XGBoost and LSTM predict next-shift hauling payload with median absolute errors around 14 to 15 percent, improving to 8.4 percent when scheduled-truck counts are added, though the unique contribution of simulated breakdown data is not demonstrated.