采矿水冲入源的快速识别模型使用随机森林,通过多策略优化,改进了子搜索算法.
Jierui Ling1, Zhibo Fu1, Kailong Xue1
1School of Coal Engineering, Shanxi Datong University Datong 037000, China.
Heliyon
|August 22, 2024
概括
这项研究引入了改进的Sparrow Search Algorithm (SSA) 来优化随机森林模型,以准确识别矿山水冲入源,提高煤矿安全.
科学领域:
- 地质科学 地质科学
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
背景情况:
- 矿井水冲入事故对煤炭开采业务构成重大威胁.
- 准确地识别冲入水源对于防止灾害至关重要.
研究的目的:
- 开发一种快速而准确的方法来识别矿井水的冲动来源.
- 通过改进的优化技术,提高随机森林算法的性能.
主要方法:
- 核心主要组件分析 (KPCA) 用于从矿山数据中提取关键因素.
- 开发并验证了一种改进的搜索算法 (ISSA).
- 使用ISSA优化随机森林 (RF) 模型的超参数.
主要成果:
- 与其他模型相比,ISSA优化的射频模型表现出优异的预测性能.
- 应用到山东省的一个矿山显示了高准确度,精度,回忆和F1指数.
- 拟议的方法证实了在识别水源的可靠性和稳定性.
结论:
- 基于KPCA的ISSA优化的射频模型为矿井水冲入源识别提供了强大而准确的方法.
- 这种方法通过为采矿生产提供可靠的保证来提高安全性.
- 该研究验证了将先进的AI算法集成到采矿安全协议中的有效性.
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