A Coalmine Water Inrush Prediction Model Based on Artificial Intelligence
Bibliographic record
Abstract
The prevention of water inrush is of great significance to the work safety in coalmines.However, the existing prediction models for coalmine water inrush cannot achieve desirable speed, accuracy, or generalization ability, owing to the complexity and diversity of causes of this accident.Therefore, this paper develops an artificial intelligence (AI)based coalmine water inrush safety prediction model, making coalmine water inrush prediction more accurate, real-time, and robust.Firstly, the causes of coalmine water inrush were combed, and used to build a reasonable evaluation index system.Next, the extreme learning machine (ELM) was optimized with particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm, and developed into a coalmine water inrush safety prediction model.The dimensionality reduction in subset classification was introduced in great details.Finally, the effectiveness of our model was proved through experiments.The research results provide the basis for the application of combinatory optimized learning machines in hazard prediction of other fields.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".