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\begin{document}

\title{Multi-time Scale Co-integration Forecast of Annual Runoff in the Source Area of the Yellow River}



\author[1]{Jinping Zhang}%
\author[2]{Hongbin Li}%
\author[2]{Qiting Zuo}%
\author[2]{Hongyuan Fang}%
\author[3]{Yang Hong}%
\affil[1]{Institute of Water Resources and Environment}%
\affil[2]{Zhengzhou University}%
\affil[3]{The University of Oklahoma}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\begin{abstract}
In order to reveal the multi-time scale of rainfall, runoff and sediment
in the source area of the Yellow River and improve the accuracy of
annual runoff forecast, the Complete Ensemble Empirical Mode
Decomposition with Adaptive Noise(CEEMDAN) method is introduced to
decompose the measured rainfall, runoff and sediment data series of the
Tangnahai hydrological station in the source area of the Yellow River of
China. With the co-integration theory, two new error correction
models(ECM) for the forecast of annual runoff in the source area of the
Yellow River are constructed. The results show that rainfall, runoff and
sediment in the source area of the Yellow River have multi-time scales
and the component sequences have co-integration relationships. For two
new ECM models, the CEEMDAN component ECM model has better forecast
accuracy than the original sequence one. The relative error of all
forecasted values is less than 15\% except 2009, and the accuracy has
reached level A.%
\end{abstract}%



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