Monthly Runoff Prediction Based on STL-CEEMDAN-LSTM Model

According to the nonlinear and non-stationary characteristics of monthly runoff sequences, the quadratic decomposition method was combined with machine learning to construct a model for predicting monthly runoff. This model uses a seasonal trend decomposition procedure based on loess (STL) to decomp...

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Bibliographic Details
Published inRen min Zhu Jiang pp. 1 - 11
Main Authors WANG Hai, SHEN Yanqing, QI Shansheng, PAN Hongzhong, HUO Jianzhen, WANG Zhance
Format Journal Article
LanguageChinese
Published Editorial Office of Pearl River 01.01.2024
Subjects
modal decomposition
neural networks for short and long-term memory
runoff prediction
upper reaches of the Yellow River
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Summary:According to the nonlinear and non-stationary characteristics of monthly runoff sequences, the quadratic decomposition method was combined with machine learning to construct a model for predicting monthly runoff. This model uses a seasonal trend decomposition procedure based on loess (STL) to decompose the measured monthly runoff sequence into trend terms, seasonal terms, and residual terms with different frequencies. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm was then applied to decompose the residual terms to obtain intrinsic mode functions (IMFs) of different frequency components. Finally, the trend term, seasonal term, and each modal component IMF were used as inputs for the long short term memory network (LSTM) for training and prediction. The model was validated with measured monthly runoff data from Tangnaihai hydrological station in the upper reaches of the Yellow River and was compared and analyzed with other models. The results show that the STL-CEEMDA
ISSN:1001-9235