Runoff prediction of lower Yellow River based on CEEMDAN–LSSVM–GM(1,1) model

Abstract

Accurate medium and long-term runoff forecasts play a vital role in guiding the rational exploitation of water resources and improving the overall efficiency of water resources use. Machine learning is becoming a common trend in time series forecasting research. Least squares support vector machine (LSSVM) and grey model (GM(1,1)) have received much attention in predicting rainfall and runoff in the last two years. “Decomposition-forecasting” has become one of the most important methods for forecasting time series data. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition method has powerful advantages in dealing with nonlinear data. Least squares support vector machine (LSSVM) has strong nonlinear fitting ability and good robustness. Gray model (GM(1,1)) can solve the problems of little historical data and low serial integrity and reliability. Based on their respective advantages, a combined CEEMDAN–LSSVM–GM(1,1) model was developed and applied to the runoff prediction of the lower Yellow River. To verify the reliability of the model, the prediction results were compared with the single LSSVM model, the CEEMDAN–LSSVM model and the CEEMDAN–support vector machines (SVM)–GM(1,1). The results show that the combined CEEMDAN–LSSVM–GM(1,1) model has a high accuracy and the prediction results are better than other models, which provides an effective prediction method for regional medium and long-term runoff prediction and has good application prospects.

The ecological–hydrological regime of the Han River basin under changing conditions: The coupled influence of human activities and climate change

“…In this study, the CEEMDAN method is utilized to decompose the original runoff data. CEEMDAN method is an improved method based on EMD (Empirical Mode Decomposition) and EEMD (Ensemble Empirical Mode Decomposition) (Guo, Wen, et al, 2023; Liu & Wang, 2021). It addresses the mode mixing issue present in the original methods, improves computational efficiency, and ensures completeness and nearly zero reconstruction errors in the decomposition process.…”

A Denoising Time Window Algorithm for Optimizing LSTM Prediction

“…This approach finds wide applications in meteorology, transportation, chemical engineering, and financial analysis, among others. Conventional mathematical techniques often employ the Grey Model (GM) to discern the dynamic behavior within sample data, aiding predictive accuracy [2] . However, with recent advances in computational capabilities, intelligent algorithms rooted in machine learning and deep learning are challenging these traditional forecasting methods.…”