Abstract: According to the characteristics of runoff forecasting in the catchement, an intelligently optimized algorithm based on recombining andimproving artificial neural network(ANN), genetic algorithm(GA) is presented in this paper. This combined algorithm can optimize the structure of neural network(NN), as well as its weights and threshold values by using the genetic algorithm which has the ability of global optimization to dynamically modify the structure and parameters of ANN and to eliminate rate tardiness of neural network training and relapsing intolocal extremum. Then, in order to verify the feasibility and validity of the combined intelligent algorithm, authors take some irrigation catchment for example and carry out serial simulation experiments by using BP, the combined intelligent algorithm respectively. The analysis results show that the combined algorithm overcomes the defects of both the blindness of structure choice and the GAs time-consuming, and improves the network`s performance and increases the speed of the network`s convergence effectually. Lastly, an dynamically intelligent interactive interface of the runoff forecasting system is developed by using the VC.net programming language.