对多个机器学习算法的比较研究,用于goaf中的风险水平预测
Bin Zhang1, Shaohua Hu1, Moxiao Li1
1School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei, 430070, China.
Heliyon
|August 28, 2023
概括
这项研究开发了一种优化的机器学习模型,用于预测地下矿山风险. 额外树算法准确地识别高风险的洞穴区域,提高了矿山的安全性.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 地质技术工程 地质技术工程
- 机器学习应用 机器学习应用
背景情况:
- 洞穴区域对地下矿山安全构成重大风险.
- 快速而准确的风险评估对于预防事故至关重要.
- 现有的评估方法可能会受到指数冗余的影响.
研究的目的:
- 为了优化功能参数,用于goaf风险水平预测.
- 确定用于Goaf风险评估的最有效的机器学习算法.
- 开发一个快速而准确的风险预测模型.
主要方法:
- 相关性分析和特征重要性被用于特征选择.
- 多个机器学习算法被应用于121组goaf数据.
- 模型性能使用准确度和kappa系数进行评估.
主要成果:
- 最优的特征参数组合包括地下水,高峰布局,高峰体积,跨度高度比率和采矿干扰.
- 额外树 (ET) 算法实现了最高的预测准确性.
- 该ET模型在Goaf风险水平预测中显示出94%的准确性.
结论:
- 优化的特征选择和额外树算法为风险预测提供了有效的解决方案.
- 开发的模型可以快速准确地评估goaf风险,提高矿山安全.
- 这种方法解决了GOAF风险评估中的索引冗余的挑战.
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