Abstract: Sediment transport capacity of slope runoff is an important hydrodynamic parameter in establishing model of soil erosion process, it is of theoretical and practical significance to study the model calculating sediment transport capacity quantitatively. By means of indoor simulated runoff-scouring experiments, sediment transport capacity of slope runoff under different slop and flow conditions was calculated. The impact factors of sediment transport capacity of slope runoff were analyzed by using method of Mean Impact Value. Generalized Regression Neural Network (GRNN) model was established, in which input variables include dry bulk density, slope, Inlet flow, outlet flow, hydraulic radius and flow rate, output variable is sediment transport capacity of slope runoff. Additionally the model was optimized by Adaboost algorithm. The validation results showed that the GRNN model was feasible to predict sediment transport capacity of slope runoff. Under conditions of experimental training samples, GRNN model performed better than BP Neural network model, and Adaboost algorithm could effectively reduce error in the prediction of GRNN model.