Prediction of runoff in the upper Yangtze river based on CEEMDAN-NAR model

Abstract

Scientific and accurate prediction of river runoff is important for river flood control and sustainable use of water resources. This study evaluates the ability of a Nonlinear Auto Regressive model (NAR) in predicting runoff volume. Using the Cuntan Hydrological Station in the upper reaches of the Yangtze River as the research object, the model was established based on the runoff characteristics from 1951 to 2020 and tested by NAR. To improve the prediction efficiency, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) preprocessing technique is used to decompose the data. The results show that the coupled CEEMDAN-NAR model has better predictive ability than the single model, with a coupled model deterministic coefficient (DC) of 0.93 and a prediction accuracy of Class A.