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Mutlu, E., Chaubey, I., Hexmoor, H. and Bajwa, S.G. (2008) Comparison of Artificial Neural Network Models for Hydrologic Predictions at Multiple Gauging Stations in an Agricultural Watershed. Hydrological Processes, 22, 5097-5106.
http://dx.doi.org/10.1002/hyp.7136

has been cited by the following article:

  • TITLE: Calibration of a Rainfall-Runoff Model to Estimate Monthly Stream Flow in an Ungauged Catchment

    AUTHORS: Shahrbanou Firouzi, Mohamad B. Sharifi

    KEYWORDS: Artificial Neural Network (ANN), Ungauged Catchments, Spatial Parameters

    JOURNAL NAME: Computational Water, Energy, and Environmental Engineering, Vol.4 No.4, October 30, 2015

    ABSTRACT: Simulation of runoff in ungauged catchments has always been a challenging issue, receiving significant attention more importantly in practical applications. This study aims at calibration of an Artificial Neural Network (ANN) model which is capable to apply in an ungauged basin. The methodology is applied to two sub-catchments located in the Northern East of Iran. To examine the effect of physical characteristics of the catchment on the capability of the model generalization, it is attempted to synthesize effective parameters using empirical methods of runoff estimation. Firstly, the model was designed for a pilot sub-catchment and the statistical comparison between simulated runoff, and target depicted the capability of ANN to accurately estimate runoff over a catchment. Then, the calibrated model was generalized to another sub-catchment assumed as an ungauged basin while there are runoff data to compare the result. The result showed that the designed model is relatively capable to estimate monthly runoff for a homogenous ungauged catchment. The method presented in this study in addition to adding effective spatial parameters in simulation runoff and calibration of model by using empirical methods and the integration of any useful accessible data, examines the adaptability of model to an ungauged catchment.