AN EXEMPLAR-BASED LEARNING-MODEL FOR HYDROSYSTEMS PREDICTION AND CATEGORIZATION

The main purpose of this paper is to represent a new Exemplar-Based Learning model and to apply this model for river flow estimation. The central idea of the model is based on a theory of learning from examples. This idea is similar to human intelligence: when people encounter new situations, they often explain them by remembering old experiences and adapting them to fit. To explore the stability and efficiency of the model performance, a simple mathematical function is simulated by the model. The model is then applied to extend the annual stream flow records according to the nearby stream flow stations and to classify the monthly flow by using the monthly rainfall and runoff information in the previous months. The results show that the model has better performance than the traditional methods and the results demonstrate the power and efficiency of the model for the hydrological data analysis.