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In view of the river pollution control and water management, this study put forward a hybrid model of autoregressive moving average (ARIMA ) model and wavelet neural network combined with genetic algorithm, to predict the river water quality. For time series data of water quality parameters, it includes linear and nonlinear sequences. So using the least square method to estimate the ARIMA model parameters, ARIMA model was used to predict linear data. For the nonlinear relationship among the residual error data, prediction result, and original data, using genetic algorithm to optimize wavelet neural network (WNN) parameters, including selection, crossover and mutation operation, WNN was applied to obtain predicted data, which increased the traditional WNN prediction precision significantly. Experimental results show that the mean absolute error of ARIMA model, wavelet neural network ,genetic algorithm optimized wavelet neural network(GAWNN), or the hybrid model without genetic algorithm optimized model prediction results are 0.29%, 0.39%, 0.26% and 0.24% respectively. The mean absolute error of the combined model prediction is about 0.19%, which is the minimum, indicating that the prediction result is better than that of single model and the hybrid model without genetic algorithm optimized.