%0 Journal Article
%T Streamflow Decomposition Based Integrated ANN Model
%A Nikhil Bhatia
%A Laksha Sharma
%A Shreya Srivastava
%A Nidhish Katyal
%A Roshan Srivastav
%J Open Journal of Modern Hydrology
%P 15-19
%@ 2163-0496
%D 2013
%I Scientific Research Publishing
%R 10.4236/ojmh.2013.31003
%X <p class=\"MsoNormal\">
	<span lang=\"EN-US\">The prediction of riverflows requires the understanding of rainfall-runoff process which is highly nonlinear, dynamic and complex in nature. In this research streamflow decomposition based integrated ANN (SD-ANN) model is devel</span><span lang=\"EN-US\">oped to improve the efficacy rather than using a single ANN model for the flow hydrograph. The streamflows are de</span><span lang=\"EN-US\">composed into two states namely </span><span lang=\"EN-US\">1</span><span lang=\"EN-US\">) the rise state and </span><span lang=\"EN-US\">2</span><span lang=\"EN-US\">) the fall state. The rainfall-runoff data obtained from the <!--?xml:namespace prefix = st1 /--><st1:place w:st=\"on\"><st1:placename w:st=\"on\">Kolar</st1:placename> <st1:placetype w:st=\"on\">River basin</st1:placetype></st1:place> is used to test the efficacy of the proposed model when compared to feed-forward ANN model (FF-ANN). The results obtained in this study indicate that the proposed SD-ANN model outperforms the single ANN model in terms of both the statistical indices and the prediction of high flows.</span><span lang=\"EN-US\"><!--?xml:namespace prefix = o /--><o:p></o:p></span> 
</p>
%K Artificial Neural Network
%K Rainfall-Runoff Modeling
%K Streamflow Decomposing
%K Black Box Modelling
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=27220