SHOW ME SOME PPT ON RAINFALL RUNOFF MODELLING

The use of artificial neural networks (RNAs) is increasingly common in the analysis of problems of hydrology and water resources. In this research, an ANN was developed to model the rainfall-runoff relationship in a basin located in a semi-arid climate in Morocco. The multilayer perceptron neural network (MLP) was chosen for use in the present study. The results and the comparative study indicate that the artificial neural network method is more adequate to predict river runoff than the classical regression model.

The rain-runoff relationship is one of the most complex hydrological phenomena to understand due to the tremendous spatial and tremendous spatial and temporal variability of watershed characteristics and precipitation patterns and the number of variables involved in the modeling of physical processes . Conceptual models provide daily, monthly, or seasonal estimates of stream flow for long-term prediction continuously. The whole physical process in the hydrological cycle is formulated mathematically in conceptual models that are composed of a large number of parameters. The accuracy of the predictions of the model is very subjective and highly dependent on the capacity of the user, knowledge and understanding of the model and the characteristics of the basin