[Objective] To provide a reference for the flood-control safety of the loess plateau check dam system

a BP neural network combination model was tried to apply for predicting runoff from a storm-flood event.[Methods] The BP neural network(BPNN) combination model(BPNNC) was constructed on the base of multiple linear regression model(MLR) and detrended cross-correlation analysis(DCCA). Its output was compared with those from other three single models(MLR

BP neural network and DCCA) by the model evaluation indexes of mean square error(MSE)

mean absolute error(MAE)

mean absolute percentage error(MAPE)

and deterministic coefficient(DC).[Results] The four values of MSE

MAE

MAPE and DC from BP neural network combination model were 2.144

5.453

0.074 and 0.988

respectively

which were better than the ones of the single models. The order of model precisions from high to low was BP neural network combination model

BP neural network model

multiple linear regression model and detrended cross-correlation analysis

successively.[Conclusion] The BP neural network combination model is more stable as compared with the single models

which can be used to predict the runoff from a storm-flood event.