| 基于多变量LSTM神经网络的地下水水位预测 |
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| 引用本文： | 闫佰忠,孙剑,王昕洲,韩娜,刘博.基于多变量LSTM神经网络的地下水水位预测[J].吉林大学学报(地球科学版),2020,50(1):208-216. |
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| 作者姓名： | 闫佰忠 孙剑 王昕洲 韩娜 刘博 |
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| 作者单位： | 1. 河北地质大学水资源与环境学院, 石家庄 050031;2. 河北省水资源可持续利用与开发重点实验室, 石家庄 050031;3. 河北省水资源可持续利用与产业结构化协同创新中心, 石家庄 050031;4. 河北省地质资源环境监测与保护重点实验室(筹), 石家庄 050031;5. 沈阳环境科学研究院, 沈阳 110167 |
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| 基金项目： | 中国博士后基金面上项目（2018M631874）；河北省自然科学基金项目（D2018403040）；河北省高等学校科学技术研究项目（ZD2019082）；河北地质大学博士科研启动基金项目（BQ2017011）；河北省水利科技计划项目（2017-59）；河北省地质资源环境监测与保护重点实验室开放基金项目（JCYKT201901）；河北省地矿局科研项目（454-0601-YBN-U1MR） |
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| 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。
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| 关 键 词： | 地下水位预测 LSTM 多变量 |
| 收稿时间： | 2019-03-23 |
Multivariable LSTM Neural Network Model for Groundwater Levels Prediction |
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| Yan Baizhong,Sun Jian,Wang Xinzhou,Han Na,Liu Bo.Multivariable LSTM Neural Network Model for Groundwater Levels Prediction[J].Journal of Jilin Unviersity:Earth Science Edition,2020,50(1):208-216. |
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| Authors: | Yan Baizhong Sun Jian Wang Xinzhou Han Na Liu Bo |
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| Abstract: | The effect of the traditional groundwater level prediction model is not ideal due to the lack of considering the temporal and spatial variation rules and the relevant influencing factors of groundwater level.The authors used the long and short term memory neural network (LSTM), adopted the method of multivariable input,and constructed the groundwater level prediction model. Taking the monitoring Well J1 in Daiyue district of Tai'an City as an example, the groundwater level dynamic monitoring data from 2001 to 2014 and the relevant influencing factor data were used to predict the groundwater level from 2015 to 2016 by using the multivariable LSTM neural network, and further compared with the single variable LSTM neural network model and BP neural network model. The research results show that the BP neural network prediction model can only make the corresponding prediction according to the change of the influencing variables without considering the change rule of timing sequence, so the prediction error is large (2.399 3). The single-variable LSTM model only takes the groundwater level as the variable input with no considering the influence of relevant factors, and the prediction error is 2.102 2. The prediction accuracy of the LSTM neural network prediction model based on multivariable input is significantly higher than that of the univariate LSTM neural network and BP neural network models, and the prediction root mean square error is only 1.919 1, which successfully verifies the accuracy of the multivariable LSTM neural network groundwater level prediction model. |
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| Keywords: | groundwater level prediction long short-term memory multivariate |
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