基于多来源数据的煤矿气体发生法预测方法融合
Huice Jiao1, Weihua Song1, Peng Cao2
1College of Mining, Liaoning Technical University, Fuxin, 123000, China.
Heliyon
|July 24, 2023
概括
这项研究引入了一种新的方法,用于使用多源数据的煤矿气体发生规律 (OL) 预测. 这种新的方法实现了98%的准确性,在效率和预测可靠性方面超过了传统方法.
科学领域:
- 地质科学 地质科学
- 采矿工程 采矿工程 采矿工程
- 数据科学数据科学数据科学
背景情况:
- 煤矿气发生法 (OL) 预测对于安全和效率至关重要.
- 现有的预测方法往往具有较低的准确性,并且耗时.
- 需要先进的方法来改善煤矿气体预测.
研究的目的:
- 为煤矿气体发生法 (OL) 开发一种新的预测方法.
- 为了提高预测准确度,并减少OL预测的时间消耗.
- 为了利用多源数据融合,改进预测建模.
主要方法:
- 数据采集和预处理煤矿气OL数据.
- 应用决策矩阵来识别关键预测数据.
- 实施多源数据融合技术来构建预测模型.
- 优化对模型输入的权重因子.
主要成果:
- 提出的方法实现了98%的预测准确度.
- 实验结果表明,与两种传统方法相比,其性能优越.
- 该方法在预测煤矿气OL时被证明是非常准确和高效的.
结论:
- 多源数据融合为煤矿天然气OL预测提供了显著的改进.
- 开发的方法提供了一种可靠和有效的工具,用于评估矿山中的气体发生情况.
- 这种方法解决了现有方法的局限性,提高了安全性和运营规划.
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