A novel model for runoff prediction based on the ICEEMDAN-NGO-LSTM coupling

Abstract The prediction of runoff trends has always been an essential topic in the field of hydrological forecasting, accurate and reliable prediction models are of great significance to the rational use of water resources. Considering the relatively-low accuracy and poor solving ability of present models for runoff prediction, a new coupled model based on the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Northern Goshawk Optimization (NGO) with Long Short-Term Memory (LSTM) model is proposed in the paper, for runoff prediction. The present model is applied to predict the monthly runoff in the middle reaches of the Huaihe River. The results show that the ICEEMDAN-NGO-LSTM model not only increases the fit by 3.09%, but also reduces the average relative error, average absolute value error, and root mean square error by 54.43, 73.11%, and 53.95%, respectively, compared with the traditional LSTM under the same data conditions. In addition, the fit of the ICEEMDAN-NGO-LSTM model was improved by 0.69% and 0.56% compared with the LSTM models optimized based on Whale Optimization Algorithm (WOA) and Imperialist Competitive Algorithm (ICA), respectively, and the average relative error, average absolute value error, and root mean square error were also significantly lower. This indicates that the coupled ICEEMDAN-NGO-LSTM model has better prediction performance, and the prediction results provide a new method for short-term runoff forecasting.