In order to overcome traditional BP neural networks problem of trapping to a local optimum in prediction of urban water quantity

the gray neutral network model (GNNM) was created by combining BP neural network with gray theory

which can realize the mutual supplement with advantages of these two methods.The PSO-GNNM (1

N) algorithm was formed by using particle swarm optimization to optimize initialized weights and threshold of GNNM.By comparison with the predicted results of traditional BP neural network and gray theory forecasting method

this algorithm had little prediction error and better generalization performance

which can provide technical support for prediction of urban water quantity.