Modeling of Rainfall-Runoff Correlations Using Artificial Neural Network-A Case Study of Dharoi Watershed of a Sabarmati River Basin, India
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.
Keywords: Artificial Neural
Networks (ANN); Feed-Forward Back Propagation Algorithm; Rainfall-Runoff
Modeling
Author: Ajaykumar Bhagubhai
Patel, Geeta S. Joshi
Journal Code: jptsipilgg170011
