Flyrock distance prediction using a hybrid LightGBM ensemble learning and two nature-based metaheuristic algorithms
Bibliographic record
Abstract
Traditional mining in open pit mines often uses explosives, leading to environmental hazards, with flyrock being a critical issue. In detail, excess flying rock beyond the designated explosion area was identified as the primary cause of fatal and non-fatal blasting hazards in open pit mining. Therefore, the accurate and reliable prediction of flyrock becomes crucial for effectively managing and mitigating associated problems. This study used the Light Gradient Boosting Machine (LightGBM) model to predict flyrock in a lead-zinc mine, with promising results. To improve its accuracy, multi-verse optimizer (MVO) and ant lion optimizer (ALO) metaheuristic algorithms were introduced. Results showed MVO-LightGBM outperformed conventional LightGBM. Additionally, decision tree (DT), support vector machine (SVM), and classification and regression tree (CART) models were trained and compared with MVO-LightGBM. The MVO-LightGBM model excelled over DT, SVM, and CART. This study highlights MVO-LightGBM's effectiveness and potential for broader applications. Furthermore, a multiple parametric sensitivity analysis (MPSA) algorithm was employed to specify the sensitivity of parameters. MPSA results indicated that the highest and lowest sensitivities are relevant to blasted rock per hole and spacing with the γ = 1752.12 and γ = 49.52, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".