Abstract: Flood forecasting is one of the important non-engineering measures for flood control and disaster reduction in the middle reaches of the Yellow River. In this study, a GRU-Transformer flood forecasting model is constructed by coupling gated recurrent units（GRU） with Transformer machine learning models, and rainfall-runoff simulations are conducted to predict flood events in typical sub-basins of the middle reaches of the Yellow River. The predictive results are compared and analyzed with those of the ANN（Artificial Neural Network） and WOA-GRU（Whale Optimization Algorithm Gate Recurrent Unit） neural network flood forecasting models, with a focus on exploring how to better apply the Transformer model to the field of flood forecasting in order to improve the accuracy of flood forecasting in the middle reaches of the Yellow River. The model is established by using historical observed flood data from 1990 to 2016 in the Gu County Reservoir Controlled Basin. The input data includes rainfall data measured at 24 stations and discharge data at the outlet cross-section, while the output data includes flood events under different lead times. The model is calibrated by using 39 flood cases and validated using 10 flood cases. The results of the study show that the GRU-Transformer model has good applicability in flood forecasting, exhibiting higher predictive accuracy in the 1～6 hour lead time flood forecasting, with NSE values of greater than 0.85 in both calibration and validation periods. Its predictive accuracy is better than the WOA-GRU and ANN models under the same lead time, but decreases to a certain extent with increasing lead times.The GRU-Transformer model is more stable and better at predicting flood peaks, showing excellent performance in predicting small flow flood processes and simulating the recession phase of floods. However, it tendes to underestimate flood peaks with longer lead times. Compared with the WOA-GRU and ANN models, the GRU-Transformer model has better robustness, and its predictive accuracy decreases slowly as lead times increases. Hence, the GRU-Transformer model can be used as one of the better flood forecasting methods, providing new forecasting methods and scientific decision-making basis for flood prevention and control in the river basin.