Runoff prediction using combined machine learning models and signal decomposition

ABSTRACT Accurate forecasting of increasingly unpredictable river runoff is essential for effective water resource management in the face of climate change and human activities. This study uses four machine learning models of long short-term memory neural networks (LSTM), support vector machine (SVM), random forest, and artificial neural network models to improve runoff forecasting accuracy and explore combined forecasting models’ effectiveness. This study develops three advanced combined forecasting models (empirical mode decomposition (EMD)–LSTM, VMD–LSTM, wavelet analysis (WA)–LSTM) by combining preprocessing techniques of EMD, variational mode decomposition (VMD), and WA with the LSTM modeling method. These models use signal decomposition techniques to analyze 41 years of runoff data from the Huanren station (1980–2020). The findings reveal that the LSTM model outperforms the other three individual machine learning models when forecasting days with high runoff. Among the three decomposed combined models, the VMD–LSTM model demonstrates the best overall performance during the validation period, achieving root mean square error, Nash–Sutcliffe efficiency coefficient, and bias values of 52.14 m3/s, 0.96, and −0.002, respectively. The combination of LSTM with signal decomposition techniques shows promising potential for enhancing runoff prediction accuracy, with practical implications for water resource management and flood control strategies.