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Accurate runoff prediction plays a pivotal role in facilitating effective watershed management and the rational allocation of water resources. Given the inherent challenges posed by the nonlinear and nonstationary nature of runoff sequences, this study introduces a novel coupled model, combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), long short-term memory (LSTM), and Informer techniques for monthly runoff prediction at the Wulong hydrological station of the Yangtze River Basin from 1973 to 2022. In addition, by comparing the prediction results of the traditional statistical model, which is the seasonal exponential smoothing model, with those of the machine learning model, the prediction accuracy of the machine learning model was found to be much higher than that of the traditional statistical mode. And the coupled model of secondary decomposition and secondary prediction was compared with other types of coupled models, such as one decomposition and one prediction. The CEEMDAN-VMD-LSTM-Informer model exhibited superior performance, as evidenced by an NSE value of 0.997, MAE of 1.327 × 10⁸ m³, MAPE of 2.57%, and RMSE of 2.266 × 10⁸ m³. The combined model proposed in this paper has the highest prediction accuracy, rendering it suitable for long-time series prediction. Accurate runoff prediction plays a pivotal role in facilitating effective watershed management and the rational allocation of water resources.