C. E. Imrie, S. Durucan and A. Korre, “River Flow Prediction Using Artificial Neural Networks: Generalization beyond the Calibration Range,” Journal of Hydrology, Vol. 233, No. 1-4, 2000, pp. 138-153. doi:10.1016/S0022-1694(00)00228-6

has been cited by the following article:

• TITLE: Streamflow Decomposition Based Integrated ANN Model

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

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

  JOURNAL NAME: Open Journal of Modern Hydrology, Vol.3 No.1, January 28, 2013

  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.