时间序列预测模型使用LSTM-变压器神经网络用于矿井水流入
Junwei Shi1, Shiqi Wang2, Pengfei Qu2
1College of Management Science and Engineering, Shandong Technology and Business University, Yantai, 264000, China. shijunwei302@sdtbu.edu.cn.
Scientific reports
|August 7, 2024
概括
预测矿井水流入对于洪水预警至关重要. 一个新的LSTM-变压器模型准确地预测了非线性,不稳定的矿井水流入,性能优于现有的方法.
科学领域:
- 地质科学 地质科学
- 数据科学数据科学数据科学
- 工程 工程师 工程师 工程师
背景情况:
- 矿山洪水事故带来了重大风险,需要准确预测矿山水的流入量,作为关键的洪水警告指标.
- 矿井水流入表现出复杂的非线性和不稳定性,对传统的预测模型构成挑战.
研究的目的:
- 开发和验证一个新的时间序列预测模型,用于矿井水流入.
- 提高矿山水流入预测的准确性和可靠性,以提高矿山安全.
主要方法:
- 开发了一种混合LSTM-变压器模型,将变压器的自我注意力与LSTM的长期依赖能力相结合.
- 采用了来自黑龙江省宝泰龙矿的数据,确定了最佳的训练测试设置比率.
- 随机搜索和贝叶斯优化用于高效的超参数调整和规范化参数选择.
主要成果:
- LSTM-变压器模型实现了最高的训练准确性,训练测试组比为7:3.
- 对比分析表明,LSTM-变压器模型的预测准确度高于LSTM,CNN,变压器和CNN-LSTM模型.
- 对于LSTM-变压器模型的所有性能指标都显示出显著的改善.
结论:
- 拟议的LSTM-变压器模型为预测非线性和不稳定的矿井水流入提供了一个高度准确和有效的解决方案.
- 这种先进的预测能力可以通过提供可靠的洪水警告,对矿山安全做出重大贡献.
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