一个新的结合智能算法预测模型用于煤炭和天然气爆发风险
Zhie Wang1, Jingde Xu2, Jun Ma3
1School of Management Engineering, Capital University of Economics and Business Beijing, Beijing, 100083, China. 12021210006@cueb.edu.cn.
Scientific reports
|September 25, 2023
概括
本研究介绍了一种改进的灰狼优化器支持向量机 (GWO-SVM) 模型,用于预测煤炭和天然气爆发. 改进后的模型实现了100%的准确性,即使缺少数据,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
- 地质工程是地质工程.
- 计算智能是一种计算智能.
背景情况:
- 煤炭和天然气爆发在采矿业务中带来了重大风险.
- 现有的这些灾害预测方法往往缺乏精度,容易受到主观解释.
- 准确和智能采矿需要强大的,数据驱动的风险评估工具.
研究的目的:
- 开发一个更准确,更可靠的煤炭和天然气爆发预测模型.
- 克服传统支向量机 (SVM) 模型的局限性,例如低噪声阻力和参数灵敏度.
- 增强灰狼优化器 (GWO) 的优化功能,以提高预测性能.
主要方法:
- 通过整合帐混乱映射和DLH策略,提出了改进的灰狼优化器 (IGWO),以增强全球和本地优化,减少本地优化.
- 开发了一个增强的预测模型,IGWO-SVM,用于煤炭和天然气爆发的预测.
- 利用随机森林 (RF) 算法识别关键预测参数并重建数据集以进行进一步的机器学习分析,创建RF-IGWO-SVM模型.
主要成果:
- 该IGWO-SVM模型表现出优于标准SVM和GWO-SVM的性能,表现出更快的训练速度和更高的分类准确性.
- 在IGWO-SVM模型中,煤炭和天然气爆发的预测准确率达到100%.
- 该RF-IGWO-SVM模型保持了100%的预测准确度,即使使用基于随机森林识别的最重要的特征的减少数据集.
结论:
- 开发的IGWO-SVM模型显著提高了煤炭和天然气爆发预测的准确性和效率.
- 随机森林的整合进一步增强了模型的稳定性,即使使用不完整的数据,也可以进行有效的预测.
- 这些发现为改善地下安全管理和促进智能采矿实践提供了有价值的工具.
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