Abstract
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.
Highlights
The rainfall-runoff relationship is one the most complex hydrological phenomenon due to the tremendous spatial and temporal variability of watershed characteristics and rainfall patterns as well as a number of variables involved in the physical processes [1]
And annual Artificial Neural Network (ANN) model with Feed-Forward Back Propagation network is developed in the present study for Dharoi watershed of Sabarmati river basin, India
The performance of the developed models was evaluated by statistical evaluation measurements, such as Pearson correlation coefficient (R) and Root Mean Square Error (RMSE)
Summary
The rainfall-runoff relationship is one the most complex hydrological phenomenon due to the tremendous spatial and temporal variability of watershed characteristics and rainfall patterns as well as a number of variables involved in the physical processes [1]. The use of basin averages for relevant parameters together with the nonlinear character of those processes leads to additional difficulties [6] These characteristics often render the implementation of conceptual model difficult and financially burdensome. The main characteristic of this type of model consists of establishing a stable relationship between input and output variables without accounting to the physical laws that govern the natural processes when rainfall is transformed into runoff. These models are easy to apply and supposedly cheaper. Examples of these models are multivariable equations with parameters estimated by Artificial Neural Networks ANNs [7]
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