What is it about?
Through fitting and predicting the annual sediment concentration in Gaocun station, it is shown that the MEEMD-ARIMA model not only considers the evolution of sediment concentration in various frequency domains, but also solves the problem that the ARIMA model requires sequence to be stable, the relative error of prediction is within ±6%, and the prediction accuracy is high, thus providing a new method for the prediction of sediment concentration.
Featured Image
Photo by Weston MacKinnon on Unsplash
Why is it important?
Through fitting and predicting the annual sediment concentration in Gaocun station, it is shown that the MEEMD-ARIMA model not only considers the evolution of sediment concentration in various frequency domains, but also solves the problem that the ARIMA model requires sequence to be stable, the relative error of prediction is within ±6%, and the prediction accuracy is high, thus providing a new method for the prediction of sediment concentration.
Perspectives
I hope that through the research content and results of this paper, we can provide a new solution and application model for sediment concentration prediction of other rivers.
Liu Fei
Read the Original
This page is a summary of: Prediction of sediment concentration based on the MEEMD-ARIMA model in the lower Yellow River, Journal of Water and Climate Change, November 2019, IWA Publishing,
DOI: 10.2166/wcc.2019.077.
You can read the full text:
Contributors
The following have contributed to this page
