基于数据增强和神经进化的煤炭和天然气爆发预测
Wenbing Shi1, Ji Huang2, Gaoming Yang1
1School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, Anhui, China.
PloS one
|February 20, 2025
概括
这项研究介绍了ANEAT,一种使用数据增强和神经进化的新方法,用于预测煤炭和天然气 (CGO) 在矿山中的爆发. ANEAT有效地解决了数据不平衡,并提高了对这种复杂的自然灾害的预测准确度.
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
- 地质危险 地质危险
背景情况:
- 煤炭和天然气喷发 (CGO) 在地下采矿中构成重大风险.
- 准确高效的CGO风险预测对于智能矿山开发至关重要.
- 现有的方法在数据不平衡和样本多样性不足方面扎.
研究的目的:
- 开发一种先进的方法来预测煤炭和天然气爆发 (CGO) 风险.
- 通过解决CGO预测中的数据限制来提高预测准确性.
- 实施一种神经进化方法来实现智能矿山安全.
主要方法:
- 提出了一种CGO风险预测方法,ANEAT,集成数据增强和神经进化算法.
- 利用点向强度转换来增强数据,处理不平衡和多样化的样本.
- 雇佣的特征重要性排序和Sparse PCA用于减小维度,入进化神经网络.
主要成果:
- ANEAT证明了最佳的CGO预测效果,MAE为0.0816,RMSE为0.1322,EVAR为0.8972. 这两种情况均为最佳的CGO预测效果.
- 通过数据增强分析,深度学习比较和群集智能算法比较验证的有效性.
- 通过轻量级架构实现了特征参数的高精度映射,以超越风险.
结论:
- ANEAT为煤炭和天然气爆发 (CGO) 预测提供了一个高度准确和高效的解决方案.
- 该方法的轻量级架构使其适合在智能矿山中的实际应用.
- ANEAT有效地克服了数据不平衡和CGO风险评估多样性的挑战.
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