基于多模型聚变的煤炭和天然气爆发预测技术的研究
Qian Xie1,2,3, Junsheng Yan1,2,3,4, Zhenhua Dai1,2,3
1CCTEG Xi'an Transparent Geology Technology Co., Ltd., Xi'an, China.
Frontiers in big data
|November 5, 2025
概括
本研究介绍了一种先进的AI框架,用于预测煤炭和天然气爆发,其性能优于单个模型. 整体模型有效地融合了多个机器学习算法,以提高采矿操作的安全性.
科学领域:
- 地质科学 地质科学
- 计算机科学 计算机科学
- 采矿工程 采矿工程 采矿工程
背景情况:
- 煤炭和天然气爆发在采矿中构成重大风险.
- 传统的预测方法在准确性和适应性方面存在局限性.
研究的目的:
- 开发一种新的AI驱动的框架,用于预测煤炭和天然气爆发.
- 通过整合多个机器学习模型来提高预测准确性.
主要方法:
- 开发了一个基于堆叠的集体学习框架.
- 基础学习者包括支持向量机器 (SVM),随机森林 (RF) 和k-最近邻居 (KNN).
- 一个注意力机制适应加权的基础学习者输出,XGBoost作为meta-learner.
主要成果:
- 整体模型在预测煤炭和天然气爆发方面明显优于单个模型 (XGBoost,SVM,RF).
- 组合方法显著改善了F1的得分.
- 用单个模型有效量化特征的重要性.
结论:
- 拟议的AI整体框架证明了煤炭和天然气爆发的强大和有效预测.
- 这种方法比矿山安全的单一模型方法有显著的进步.
- 融合战略利用了各种AI算法的互补优势.
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