摘要
由于岩溶地下水具有强烈的非线性及非平稳波动特征,水位预测结果容易产生较大误差。针对岩溶地下水水位预测精度较差的问题,提出一种EMD-LSTM耦合模型,首先采用经验模态分解(EMD)将趵突泉岩溶地下水水位分解为5个分量(4个本征模函数项和1个残余项),以此消除水位数据的非平稳波动性;同时构建长短期记忆(LSTM)神经网络模型,并将与地下水水位动态变化密切相关的降水量(表征含水层补给项)和月平均气温值、月最高气温值、月最低气温值、水汽压值(表征含水层排泄项)作为输入项分别对5个分量进行预测,最终将分量预测结果累加获得地下水水位预测值。结果表明:EMD能够显著消除岩溶地下水水位的非平稳波动特征;EMD-LSTM耦合模型可有效提高岩溶地下水水位的预测精度,其均方根误差相比于LSTM神经网络模型、ARIMA模型分别减小了27.86%和59.94%。总体来说,本文所提出的EMD-LSTM耦合模型具有较强的可靠性和稳定性,可为岩溶地下水水位的精确预测提供借鉴。
The prediction of karst groundwater level is prone to significant errors due to the strong nonlinear and non-stationary fluctuation characteristics of karst groundwater.Addressing to the poor prediction accuracy problem,an EMD-LSTM coupled model is proposed.Firstly,empirical mode decomposition(EMD)is used to decompose the karst groundwater level of Baotu Spring into five components(four intrinsic mode function terms and one residual term),in order to eliminate non-stationary fluctuations in water level data.At the same time,a long short-term memory(LSTM)neural network model is constructed,and the indices that are closely related to the dynamic change of groundwater level,such as rainfall(representing the aquifer recharge term),monthly average temperature,monthly maximum temperature,monthly minimum air temperature,and water vapor pressure(representing the aquifer discharge term),are used as input items to predict the five components respectively,and finally the prediction results of the components are summed up to obtain the prediction value of the groundwater level.The results indicate that EMD can significantly eliminate the non-stationary fluctuation characteristics of karst groundwater level;the EMD-LSTM coupled model can effectively improve the accuracy of karst groundwater level prediction,with a root mean square error reduction of 28.22%and 59.94%compared to the LSTM model and ARIMA model,respectively.Overall,the proposed EMD-LSTM coupling model has strong reliability and stability,which can provide some reference for accurate prediction of karst groundwater level.
作者
侯金霄
黄林显
胡晓农
钱永
邢立亭
韩忠
梁浩
HOU Jinxiao;HUANG Linxian;HU Xiaonong;QIAN Yong;XING Liting;HAN Zhong;LIANG Hao(School of Water Conservancy and Environment,University of Jinan,Jinan 250022,China;Institute of Hydrogeology and Environmental Geology,Chinese Academy of Geological Sciences,Shijiazhuang 050061,China;No.6 Institute of Geology and Mineral Resources Exploration of Shandong Province,Weihai 264209,China;Shandong Provincial Territorial Spatial Ecological Restoration Center,Jinan 250014,China)
出处
《水资源与水工程学报》
CSCD
北大核心
2023年第4期92-98,共7页
Journal of Water Resources and Water Engineering
基金
国家自然科学基金项目(42272288)
山东省自然科学基金项目(ZR2019MD029)
山东省高校院所创新团队项目(2021GXRC070)。
关键词
岩溶地下水水位预测
经验模态分解
长短期记忆神经网络
趵突泉
prediction of karst groundwater level
empirical mode decomposition(EMD)
long short-term memory(LSTM)neural network
Baotu Spring
