基于VMD-CNN-LSTM模型的渭河流域月径流预测  

Monthly runoff prediction of Weihe River Basin based on VMD-CNN-LSTM model

作　　者：包苑村 解建仓[1] 罗军刚[1] BAO Yuancun;XIE Jiancang;LUO Jungang(State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China,Xi’an University of Technology,Xi’an 710048,China)

机构地区：[1]西安理工大学西北旱区生态水利国家重点实验室,陕西西安710048

出　　处：《西安理工大学学报》2021年第1期1-8,共8页Journal of Xi'an University of Technology

基　　金：国家自然科学基金资助项目(51679186);西安理工大学省部共建西北旱区生态水利国家重点实验室自主研究课题资助项目(2019KJCXTD-5)。

摘　　要：为了提高月径流预测的精度,解决月径流的非线性和不稳定性对于预测结果的影响,提出了变分模态分解(VMD)、卷积-长短期记忆神经网络(CNN-LSTM)组合的VMD-CNN-LSTM模型。通过选取渭河流域张家山站和魏家堡站1960—2005年的实测月径流数据进行训练和测试,并与EMD-LSTM、EMD-CNN-LSTM、VMD-LSTM模型进行对比,选取均方根误差(RMSE)、平均绝对误差(MAE)和Nash系数(NSE)对测试集的预测结果进行评价。研究结果表明VMD-CNN-LSTM模型相较于其他模型有更优的预测精度以及更低的误差,且对于峰值谷值的拟合更精确,为月径流的预测提供了一条新的途径。In order to improve the accuracy of monthly runoff prediction and to solve the influence of the non-linearity and instability of monthly runoff on the prediction results,a VMD-CNN-LSTM model combining variational modal decomposition(VMD)and convolution-long short-term memory neural network(CNN-LSTM)is proposed.Training and testing are carried out by selecting the measured monthly runoff data from Zhangjiashan Station and Weijiabao Station in the Weihe River Basin from 1960 to 2005,comparing with EMD-LSTM,EMD-CNN-LSTM,VMD-LSTM models,selecting root mean square error(RMSE),mean absolute error(MAE)and Nash coefficient(NSE)to evaluate the prediction results of the test set.The results show that the VMD-CNN-LSTM model has better prediction accuracy and lower error than other models.The fitting of peaks and valleys is more accurate,providing a new way for the prediction of monthly runoff.

关 键 词：变分模态分解 神经网络 径流预测 渭河流域 

分 类 号：TV11[水利工程—水文学及水资源]