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 models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input variables
Highlights
Rainfall-runoff is important in activities such as flood control and management, design of hydraulic structures in a watershed, and likewise
The results demonstrate that Artificial Neural Network (ANN) models are able to provide a good representation of an event-based rainfall-runoff process
Coefficient of correlation (R) is another indicator of goodness of fit and it is seen from Table 3 that, R is quite high in all the cases of training and tested data sets
Summary
Rainfall-runoff is important in activities such as flood control and management, design of hydraulic structures in a watershed, and likewise. Deterministic models of varying degrees of complexity have been employed in the past for the rainfall runoff process with varying degrees of success. The rainfall runoff is a complex, dynamic, and non-linear process, which is affected by many and often interrelated, physical factors. The influence of these factors and many of their combinations in generating runoff is an extremely complex physical process, and is not clearly understood [1]. Many of the deterministic rainfall-runoff models need a large amount of data for calibration and validation purposes, and are computationally expensive. The use of deterministic models of the rainfall-runoff process is viewed rather skeptically by researchers and has not become very popular [2]
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