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基于LSTM神经网络的煤矿突水预测

董丽丽 费城 张翔 曹超凡

董丽丽, 费城, 张翔, 曹超凡. 基于LSTM神经网络的煤矿突水预测[J]. 煤田地质与勘探, 2019, 47(2): 137-143. doi: 10.3969/j.issn.1001-1986.2019.02.021
引用本文: 董丽丽, 费城, 张翔, 曹超凡. 基于LSTM神经网络的煤矿突水预测[J]. 煤田地质与勘探, 2019, 47(2): 137-143. doi: 10.3969/j.issn.1001-1986.2019.02.021
DONG Lili, FEI Cheng, ZHANG Xiang, CAO Chaofan. Coal mine water inrush prediction based on LSTM neural network[J]. COAL GEOLOGY & EXPLORATION, 2019, 47(2): 137-143. doi: 10.3969/j.issn.1001-1986.2019.02.021
Citation: DONG Lili, FEI Cheng, ZHANG Xiang, CAO Chaofan. Coal mine water inrush prediction based on LSTM neural network[J]. COAL GEOLOGY & EXPLORATION, 2019, 47(2): 137-143. doi: 10.3969/j.issn.1001-1986.2019.02.021

基于LSTM神经网络的煤矿突水预测

doi: 10.3969/j.issn.1001-1986.2019.02.021
基金项目: 

国家自然科学基金项目(61272458);陕西省自然科学基金项目(2016JM6031);西安市科技创新引导项目(201805033YD11CG17(1),201805033YD11CG17(2))

详细信息
    第一作者:

    董丽丽,1960年生,女,福建福州人,教授,从事数据挖掘、机器学习方面的研究.E-mail:donglilixjd@163.com

  • 中图分类号: TD745

Coal mine water inrush prediction based on LSTM neural network

Funds: 

National Natural Science Foundation of China(61272458)

  • 摘要: 针对煤层底板突水预测问题,在总结现有突水预测方法和理论的基础上,通过特征选择实验得出水压、距工作面距离、砂岩段厚度、煤层厚度、煤层倾角、断层落差、裂隙带、开采面积、采高、走向长度是影响突水发生的主要因素,这些因素具有复杂、非线性的特点。提出基于长短时记忆(LSTM)神经网络构建的突水预测模型,将煤矿突水实例的数据作为样本数据对模型进行训练。最后,将LSTM神经网络模型与遗传算法-反向传播(GA-BP)神经网络模型和反向传播(BP)神经网络模型进行对比实验。实验结果表明,LSTM神经网络模型在测试集上的预测正确率更高,稳定性更好,更适用于煤层底板突水预测。

     

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出版历程
  • 收稿日期:  2018-05-18
  • 发布日期:  2019-04-25

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