Combination of Neural Networks and Genetic Algorithms, an Approach to Estimate the Flood Flow

Fast and accurate estimation in Peak flow is one of the major issues in water resources management that have basic role in the design of hydraulic structures and biological activities in drainage basins. So that a proper assessment has a basic role in the success of administrative tasks. In this paper, using artificial intelligence methods (Multi-layer Perceptron Neural Network and the mixture of Multi-layer Perceptron Neural Network with genetic algorithm) to estimate yalfan river, s peak discharge in Yalfan,s sediment and hydrometer local gaging station. For these two models, 8 variables have been considered as the inputs that includes rainfall related to day of peak flow,5 days rainfall that occurs before of the flooding day, curve number of the basin(CN) and base discharge and finally peak discharge is considered as the output. With artificial intelligence after preprocessing of the data, the optimal structure of the model is determined with input and output data by evaluation criteria and trial and error. In the final, with the mixture of artificial network and genetic algorithm model, the optimum neural network model was determined which results were an input to genetic algorithm model, this has been a good performance in runoff forecasting in Yalfan Basin.