Streamflow Decomposition Based Integrated ANN Model

Nikhil Bhatia, Laksha Sharma, Shreya Srivastava, Nidhish Katyal, Roshan Srivastav

School of Mechanical and Building Sciences, VIT University, Vellore, India..

DOI: 10.4236/ojmh.2013.31003                

Abstract

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 developed to improve the efficacy rather than using a single ANN model for the flow hydrograph. The streamflows are decomposed into two states namely 1) the rise state and 2) the fall state. The rainfall-runoff data obtained from the Kolar River basin 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.

Keywords

Artificial Neural Network; Rainfall-Runoff Modeling; Streamflow Decomposing; Black Box Modelling

Conflicts of Interest

The authors declare no conflicts of interest.

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