- Title
- Performance of hybrid SCA-RF and HHO-RF models for predicting backbreak in open-pit mine blasting operations
- Creator
- Zhou, Jian; Dai, Yong; Khandelwal, Manoj; Monjezi, Masoud; Yu, Zhi; Qiu, Yingui
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/179546
- Identifier
- vital:15637
- Identifier
-
https://doi.org/10.1007/s11053-021-09929-y
- Identifier
- ISBN:1520-7439 (ISSN)
- Abstract
- Backbreak is an adverse phenomenon in blasting operation, which can cause, among others, mine walls instability, falling down of machinery, drilling efficiency reduction and stripping ratio enhancement. Therefore, this research aimed to develop two-hybrid RF (Random Forest) prediction models of random forest, which are optimized by Harris hawks optimizer (HHO) and sine cosine algorithm (SCA), for estimation of the backbreak distance. The HHO and SCA algorithms were adopted to determine two hyper-parameters (mtry and ntree) in the RF models, in which root mean square error (RMSE) was utilized as a fitness function. A database with 234 samples was established, in which six variables [i.e., hole length (L), burden (B), spacing (S), stemming (T), special drilling (SD) and powder factor (PF)] were used as input variables, and backbreak was defined as output variable. Additionally, three classical regression models (i.e., extreme learning machine, radial basis function network and general regression neural network) were adopted to verify the superiority of the hybrid RF prediction models. The predictive reliability of the proposed models was assessed by the combination of mean absolute error (MAE), RMSE, variance accounted for (VAF) and Pearson correlation coefficient (R2). The results revealed that the SCA-RF model outperformed all the other prediction models with MAE of (0.0444 and 0.0470), RMSE of (0.0816 and 0.0996), VAF of (96.82 and 95.88) and R2 of (0.9876 and 0.9829) in training and testing stages, respectively. A Gini index generated internally in the RF model showed that backbreak was significantly more sensitive to L and T than to SD. © 2021, International Association for Mathematical Geosciences.
- Publisher
- Springer
- Relation
- Natural Resources Research Vol. 30, no. 6 (2021), p. 4753-4771
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2021 International Association for Mathematical Geosciences
- Rights
- Open Access
- Subject
- 0502 Environmental Science and Management; 0914 Resources Engineering and Extractive Metallurgy; Backbreak; Hybrid RF model; Metaheuristic optimization; Optimization; Prediction
- Full Text
- Reviewed
- Funder
- This research was funded by the Innovation-Driven Project of Central South University (No. 2020CX040) and the National Natural Science Foundation of China (Grant Nos. 72088101 and 41807259).
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