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Outline
Rockburst intensity prediction is a critical task in mine safety management. To improve prediction accuracy and practical applicability, a rockburst intensity prediction model named NRBO-BPNN that combined the Newton-Raphson Based Optimizer (NRBO) with a BP Neural Network (BPNN) was proposed. By integrating a gradient-guided search mechanism based on the Newton-Raphson method, the model effectively overcomes the problem that the traditional BPNN is easy to fall into the local optimal solution during the training process, and significantly improves the global search ability and convergence efficiency. Experimental comparisons demonstrated that the NRBO-BPNN model achieved an accuracy of 100% on the training set and 86.67% on the testing set. The total accuracy rate is 96%, and only four misjudgments occur. It is superior to common models such as traditional BPNN, Support Vector Machine (SVM), Random Forest (RF) and Extreme Learning Machine (ELM). Finally, a comprehensive system for predicting rockburst intensity was developed based on the proposed model and successfully applied to on-site data analysis of Maluping Mine and Dongguashan Copper Mine, verifying its engineering applicability and practical value. The research results indicate that NRBO-BPNN can provide more efficient and accurate decision support for mining engineering safety design, and has significant potential for engineering promotion.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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