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R. K. Srivastav, K. P. Sudheer and I. Chaubey, “A Simplified Approach to Quantifying Predictive and Parametric Uncertainty in Artificial Neural Network Hydrologic Models,” Water Resources Research, Vol. 31, No. 10, 2007, pp. 2517-2530.

has been cited by the following article:

• TITLE: Dual Artificial Neural Network for Rainfall-Runoff Forecasting

  AUTHORS: Pallavi Mittal, Swaptik Chowdhury, Sangeeta Roy, Nikhil Bhatia, Roshan Srivastav

  KEYWORDS: Forecasting; Hybrid model; ANN; Floods; Non Linear

  JOURNAL NAME: Journal of Water Resource and Protection, Vol.4 No.12, December 13, 2012

  ABSTRACT: One of the principal issues related to hydrologic models for prediction of runoff is the estimation of extreme values (floods). It is well understood that unless the models capture the dynamics of rainfall-runoff process, the improvement in prediction of such extremes is far from reality. In this paper, it is proposed to develop a dual (combined and paralleled) artificial neural network (D-ANN), which aims to improve the models performance, especially in terms of extreme values. The performance of the proposed dual-ANN model is compared with that of feed forward ANN (FF-ANN) model, the later being the most common ANN model used in hydrologic literature. The forecasting exercise is carried out for hourly river flow data of Kolar Basin, India. The results of the comparison indicate that the D-ANN model performs better than the FF-ANN model.