Underground mine truck travel time prediction based on stacking integrated learning. (April 2023) - Li, Ning; Wu, Yahui; Wang, Qizhou; Ye, Haiwang; Wang, Liguan; Jia, Mingtao; Zhao, Shugang
Abstract: The travel time (TT) prediction of underground mine transport trucks provides essential information for the precise scheduling of mine intelligent dispatching systems. Given the operational requirements and transportation environment of underground mines, in this study, a TT prediction method for underground mine transportation trucks is proposed based on stacking integrated learning. First, depending on the position and status of the transport truck, the truck operation cycle process is broken down into three sections and six stages. The influencing factors of the trucks' TT in each stage are determined from the perspectives of personnel, equipment, and environment. During the collection process of the influencing factors the road surface roughness data are collected through image processing as part of the influence factor data. The influencing factors' data are used as input parameters for the stacking integrated learning prediction model. The prediction performance of the fusion model is compared with that of the single models and their pairwise combinations. The final prediction results show that the fusion model performs the best in the drifts, ramps, and ground road sections. The average absolute percentage errors of the predicted values in the three road sections are 2.3091%, 4.3906%, and 4.5583%, respectively, and the corresponding decision coefficients are 0.9890, 0.9801, and 0.9050. These results show that the prediction model based on the stacking integrated framework proposed in this paper has a high prediction accuracy and stability. This accurate model can meet the requirements of intelligent dispatching systems for underground mines. Highlights: Studies the prediction of truck transportation travel time in underground mines. Refines the underground mine truck transportation flow and predicts truck travel time by road section. Analyzes the influencing factors of truck travel time from the perspectives of personnel, equipment and environment. A multi-model ensemble learning prediction model is constructed based on Stacking ensemble learning framework.