吉林大学学报(地球科学版) ›› 2020, Vol. 50 ›› Issue (1): 208-216.doi: 10.13278/j.cnki.jjuese.20190055
• 地质工程与环境工程 • 上一篇
闫佰忠1,2,3, 孙剑1,2,3, 王昕洲4, 韩娜1,2,3, 刘博5
Yan Baizhong1,2,3, Sun Jian1,2,3, Wang Xinzhou4, Han Na1,2,3, Liu Bo5
摘要: 为解决以往模型未考虑地下水位相关影响因素的问题,探讨长短期记忆(LSTM)神经网络在地下水位预测中的应用,利用长短期记忆神经网络,采用多变量输入的方式,构建了基于多变量LSTM神经网络的地下水水位预测模型。以泰安市岱岳区J1号监测井为例,采用2001-2014年地下水水位动态监测资料与相关影响因素数据,利用多变量LSTM神经网络对2015-2016年地下水位进行预测,并与单变量LSTM神经网络和反向传播(BP)神经网络进行对比。研究结果表明:以相关影响变量为输入的BP神经网络无法考虑时序变化规律,预测均方根误差最大,为2.399 3;以地下水位为变量输入的单变量LSTM神经网络仅能根据时序变化作出相应预测,无法考虑相关变量影响,预测均方根误差为2.102 2;基于多变量输入的LSTM神经网络的预测精度显著高于单变量LSTM神经网络和BP神经网络,预测均方根误差最小,仅为1.919 1。总体上,多变量LSTM神经网络地下水位预测模型仅在某些峰值处误差较大,但总体预测效果较为理想。
中图分类号:
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[1] | 刘博, 肖长来, 梁秀娟. SOM-RBF神经网络模型在地下水位预测中的应用应用[J]. 吉林大学学报(地球科学版), 2015, 45(1): 225-231. |
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