Due to the nonlinearity of residual chlorine in the pipe network, a PSO-SVM and BP neural network combined model was developed to prediction of residual chlorine.This model through particle swarm optimization algorithm (PSO) to optimization the characteristics parameter of the SVM, and use the BP neural network model to residual error correction. The prediction precision of combined model was ananysed by comparing the single prediction model of BP and SVM. The results show that compared with the single prediction of BP and SVM, the mean square error of the combined model decreased by 62.30% and 75.29% respectively, but the average relative error decreased by 55.03% and 54.27% respectively. In a conclusion, the combined model had strong nonlinear fitting capability, high prediction accuracy, and strong operation stability. This model plays an important role in controlling the residual chlorine dosing and setting the secondary chlorination point for water supply enterprise.