基于IAHA-SVM的煤矿环境安全警告模型
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种改进的人工蜂鸟算法 (IAHA),用于优化煤矿安全的支持矢量机 (SVM) 模型. 通过准确地分类地下气候环境,IAHA-SVM模型增强了早期预警系统,提高了工人安全.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 煤炭开采至关重要,但会带来严重的地下安全风险.
- 事故往往源于危险的地下气候条件.
- 及时检测环境异常对于工人安全至关重要.
研究的目的:
- 为煤矿环境安全开发先进的早期预警模型.
- 使用人工智能提高安全评估的准确性和效率.
- 改进地下气候异常的检测.
主要方法:
- 使用改进的人工蜂鸟算法 (IAHA) 优化支持向量机 (SVM) 参数.
- 将帐混沌映射,向后学习和Levy飞行纳入IAHA以提高性能.
- 使用一个简单的方法来完善算法的代过程.
- 根据环境参数将煤矿安全分为四个级别.
主要成果:
- 与标准算法相比,IAHA证明了较好的融合速度和搜索准确性.
- 通过IAHA-SVM模型,在分类煤矿环境安全方面实现了显著的性能提升.
- 开发的模型有效预测安全水平,有助于及时干预.
结论:
- IAHA-SVM模型为煤矿安全预警系统提供了强大而准确的解决方案.
- 人工蜂鸟算法的改进提高了它在复杂的优化任务中的适用性.
- 这种方法有助于减轻风险,并确保地下煤矿的安全工作环境.
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