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Runoff time series prediction based on hybrid models of two-stage signal decomposition methods and LSTM for the Pearl River in China

文献类型： 外文期刊

作者： Guo, Zhao 1 ; Zhang, Qian-Qian 1 ; Li, Nan 1 ; Zhai, Yun-Qiu 1 ; Teng, Wen-Tao 1 ; Liu, Shuang-Shuang 4 ; Ying, Guang-Guo 1 ;

作者机构： 1.South China Normal Univ, Environm Res Inst, Guangdong Prov Key Lab Chem Pollut & Environm Safe, Guangzhou 510006, Peoples R China

2.South China Normal Univ, MOE Key Lab Theoret Chem Environm, Guangzhou 510006, Peoples R China

3.South China Normal Univ, Sch Environm, Guangzhou 510006, Peoples R China

4.Chinese Acad Fishery Sci, South China Sea Fisheries Res Inst, Guangdong Prov Key Lab Fishery Ecol & Environm, Guangzhou 510300, Peoples R China

关键词： large-scale basin; LSTM; signal decomposition; time series runoff prediction; wavelet transformation

期刊名称：HYDROLOGY RESEARCH （ 影响因子：2.7； 五年影响因子：2.6 ）

ISSN： 1998-9563

年卷期： 2023 年

页码：

收录情况： SCI

摘要： Hydrological runoff prediction is vital for water resource management. The non-linear and non-stationary runoff series and the complex hydrological features for large-scale basins make it difficult to predict. Long short-term memory (LSTM) is effective for runoff prediction but unstable for large-scale basins. This study develops three hybrid models combined with two-stage decomposition and LSTM, including wavelet transformation (WT) combined with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), and local mean decomposition (LMD), to predict the daily runoff of the Pearl River in China. The results indicate CEEMDAN's broader signal decomposition applicability for runoff series preprocessing, while VMD is simpler to extract high-runoff characteristics. VMD-WT-LSTM is appropriate for predicting high and median runoff, whereas CEEMDAN-WT-LSTM is better for low-runoff and high and median runoffs with low-violent fluctuations. These hybrid models provide satisfactory predictions for NSE and R2 indicators, and 97.2% of indicators fall within the acceptable range for high-runoff predictions. The hybrid models outperform traditional and standalone models in high-runoff but none of the decomposition methods in this research can identify low-runoff sub-sequence. This study provided runoff prediction methods requiring fewer data and processing time, and these methods are promising alternatives for daily runoff prediction in large-scale basins.

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