摘要
为了有效提高非平稳非线性径流序列的预测精度,采用具有自适应序列特征的时变滤波经验模态分解(TVF-EMD)与长短期记忆神经网络(LSTM)耦合,构成TVF-EMD-LSTM预测模型。首先利用TVF-EMD方法自适应地将径流序列分解为高频序列和低频序列;进而,利用LSTM神经网络对分解后的序列分别预测;最终,将预测结果加和重构为最终径流预测结果。提出的模型应用于洛河流域长水水文站月径流预测,并与LSTM模型、EMD-LSTM模型和CEEMDAN-LSTM模型进行对比。结果表明:TVF-EMD-LSTM神经网络耦合模型预测精度最高,预测误差最小。由此可见,TVF-EMD能更好地缓解模态混叠问题,可为径流序列的数据预处理提供更好的方式,提出的TVF-EMD耦合模型也为月径流预测提供了一种有效的新方法。
In order to effectively improve the prediction accuracy of non-stationary and non-linear runoff series,the TVF-EMD-LSTM prediction model is proposed by coupling the time varying filter empirical mode decomposition(TVF-EMD)with adaptive sequence characteristics and Long Short-Term Memory neural network(LSTM).Firstly,the TVF-EMD method is used to adaptively decompose the runoff series into high-frequency series and low-frequency series;secondly,the LSTM neural network is used to predict the decomposed series.Finally,the predicted results are added and reconstructed into the final runoff prediction results.The proposed model is applied to the monthly runoff forecast of the Changshui Hydrological Station in the Luohe River Basin,and compared with LSTM model,EMD-LSTM model and CEEMDAN-LSTM model.The results show that the TVF-EMD-LSTM neural network coupling model has the highest prediction accuracy and the smallest error.Therefore,TVF-EMD can better alleviate the mode mixing problem and provide a better way for the data preprocessing of runoff series.The TVF-EMD-LSTM hybrid model provides an effective new method for monthly runoff forecasting.
作者
王文川
高畅
徐雷
WANG Wen-chuan;GAO Chang;XU Lei(College of Water Resources,North China University of Water Resources and Electric Power,Zhengzhou 450046,China)
出处
《中国农村水利水电》
北大核心
2022年第2期76-81,89,共7页
China Rural Water and Hydropower
基金
国家自然科学基金项目(51509088,51709108)
河南省重点研发与推广专项(202102310259,202102310588)
河南省高校科技创新团队(18IRTSTHN009)。
关键词
月径流预测
时变滤波经验模态分解
长短期记忆神经网络
耦合模型
长水水文站
monthly runoff forecast
time varying filter based empirical mode decomposition
long-short term memory neural network
coupling model
Changshui Hydrological Station
