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

Abstract

Prediction of runoff trends is a critical topic in hydrological forecasting. Accurate and reliable prediction models are important for the rational use of water resources. This paper proposes a new coupled model, ICEEMDAN-NGO-LSTM, for runoff prediction in the middle reaches of the Huai River. This model combines the excellent nonlinear processing capability of the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm, the perfect optimization strategy of the Northern Goshawk Optimization (NGO) algorithm, and the advantages of the Long Short-Term Memory (LSTM) algorithm in modeling time series data. The results show that the ICEEMDAN-NGO-LSTM model predicts the monthly runoff trend with higher accuracy compared to the actual data variation. The average relative error is 5.95% within 10%, and the Nash Sutcliffe (NS) is 0.9887. These results indicate that the ICEEMDAN-NGO-LSTM coupled model has superior prediction performance and provides a new method for short-term runoff forecasting.

Keywords: Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise; Long Short-Term Memory; Middle of Huai River; Northern Goshawk Optimization; Runoff prediction.