modeling of rainfall-runoff correlations using artificial neural network-a case study of dharoi watershed of a sabarmati river basin, india

the use of an artificial neural network (ann) is becoming common due to its ability to analyse complex nonlinear events. an ann has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. this capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature. artificial neural networks (ann) can be used in cases where the available data is limited. the present work involves the development of an ann model using feed-forward back propagation algorithm for establishing monthly and annual rainfall runoff correlations. the hydrologic variables used were monthly and annual rainfall and runoff for monthly and annual time period of monsoon season. the ann model developed in this study is applied to dharoi reservoir watersheds of sabarmati river basin of india. the hydrologic data were available for twenty-nine years at dharoi station at dharoi dam project. the model results yielding into the least error is recommended for simulating the rainfall-runoff characteristics of the watersheds. the obtained results can help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought.