基于优化变量模式分解和SSA-LSTM的煤炭采矿面气体排放时间序列预测
Jingzhao Zhang1, Yuxin Cui1, Zhenguo Yan1
1College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究引入了一种新的GA-VMD-SSA-LSTM模型,用于准确预测煤矿的气体排放. 该模型显著提高了预测准确度,有助于防止天然气灾害.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 准确的气体排放预测对于防止采矿灾害至关重要.
- 现有的模型需要提高矿山面的预测准确度.
研究的目的:
- 开发一种先进的模型,用于提高煤矿开采业务中的气体排放预测.
- 提高气体排放预测的准确性和可靠性.
主要方法:
- 提出了一个GA-VMD-SSA-LSTM模型,整合了遗传算法 (GA),变化模式分解 (VMD),子搜索算法 (SSA) 和长短期记忆 (LSTM).
- 开发了一个VMD评估标准,以使用GA优化VMD参数.
- 优化了LSTM参数与SSA用于预测模型.
- 预测和叠加的个人组件预测,用于最终的气体排放预测.
主要成果:
- 与VMD-LSTM,SSA-LSTM和高斯过程回归 (GPR) 模型相比,GA-VMD-SSA-LSTM模型表现出卓越的性能.
- 在不同的预测集大小中实现了0.95,0.96和0.99的高匹配度R2值.
- 验证了模型在采矿面的时间序列气体排放预测中的有效性.
结论:
- 拟议的GA-VMD-SSA-LSTM模型显著提高了气体排放预测的准确性.
- 该模型提供了一种可靠的工具,用于指导煤矿天然气灾害的预防和控制.
- 这种方法为在具有挑战性的采矿环境中进行时间序列预测提供了一个有希望的解决方案.
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