基于BO-CatBoost模型的车道中平坦的石支的安全因子预测的研究
Haiping Yuan1,2, Shuaijie Ji2, Chuanqi Zhu1
1State Key Laboratory of Mining Induced Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, China.
Biomimetics (Basel, Switzerland)
|July 26, 2024
概括
一个新的机器学习模型,BO-CatBoost,准确地预测了采矿操作的车道安全因素. 这种方法通过优化车道支设计来提高采矿开采的安全性和效率.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 计算科学 计算科学
- 地质技术工程 地质技术工程
背景情况:
- 采矿车道的传统实证支持系统缺乏评估支持合理性的强有力的方法,可能会损害安全.
- 确保安全和高效的采矿开采需要一个合理的车道支持方案.
研究的目的:
- 为使用安全因子的车道支持系统开发精确的安全评估方法.
- 提出和验证一种机器学习模型,用于预测在平坦的混凝土支上使用车道安全因素.
主要方法:
- 使用CRITIC方法对铁矿车道数据进行数据预处理.
- 采用贝叶斯算法来优化CatBoost模型的超参数,创建BO-CatBoost模型.
- 使用RMSE,MAE,R2,VAF和a-20指数等指标评估模型性能.
主要成果:
- 与其他模型相比,BO-CatBoost模型表现出优越的预测性能.
- 在测试组中实现了最低的RMSE和MAE,以及最高的R2和VAF.
- 该模型提供了适当的a-20指数值,表明安全因子预测的高准确性.
结论:
- BO-CatBoost模型是最适合的机器学习方法,用于准确预测采矿车道安全因素.
- 这种新的方法为优化车道支设计和加强采矿安全评估提供了巨大的潜力.
- 该研究提供了一个数据驱动的框架,用于提高采矿操作的可靠性.
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