Artificial Neural Networks for Event Based Rainfall-Runoff Modeling

Abstract

The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN mod- els have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in mod- eling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very ac- curately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input variables.