基于神经网络和ARIMA模型的优化模型的构建和应用,用于基于神经网络和ARIMA模型的矿井水流入预测
Xiaoyu Gong1, Bo Li2, Yu Yang1
1Key Laboratory of Karst Georesources and Environment, College of Resources and Environmental Engineering, Guizhou University, Ministry of Education, Guizhou University, Guiyang, 550025, China.
Scientific reports
|January 15, 2025
概括
矿井水的流入构成了重大的地质危险. 一个新的BP-ARIMA模型准确地预测了矿井水的流入,改善了安全和预防策略.
科学领域:
- 地质工程是地质工程.
- 数据科学数据科学数据科学
- 风险管理 风险管理
背景情况:
- 矿山水流入是一个复杂的地质危险,影响煤炭生产和矿山安全.
- 预测矿井水流入对于有效防止水损害至关重要.
- 现有的预测方法面临的挑战是由于水流入的非线性和复杂性.
研究的目的:
- 开发一个更准确的预测模型,用于矿井水的流入.
- 加强矿山水损害预防战略的科学基础.
- 提高矿山工作人员的安全和煤炭生产质量.
主要方法:
- 通过整合BP神经网络和ARIMA模型,建立了一个BP-ARIMA预测模型.
- 从2020年1月到2023年2月使用的矿井水流入的时间序列数据.
- 使用绝对相对误差,比较了BP-ARIMA模型与BP,ARIMA,大井方法和GM(1,1) 模型的性能.
主要成果:
- 该BP-ARIMA(3,1,1) 模型表现出卓越的准确性,平均绝对相对误差为1.02%.
- 达到0.93的高合适度 (R2),明显超过单个模型.
- 为矿山水流入提供了准确的6个月预测 (2022年7月至2023年2月).
结论:
- 该BP-ARIMA模型大大提高了矿山水流入的预测准确度.
- 为未来的矿山水流入预测和预防提供可靠的科学基础.
- 通过减轻与水有关的危险,提高矿山安全和运营效率.
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