Precipitation-runoff simulation for a Himalayan River Basin,
India using artificial neural network algorithms |
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| Authors: | Ray Singh Meen Ramakar Jha and Kishanjit Kumar Khatua |
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| Institution: | Department of Civil Engineering, National Institute of Technology, Rourkela, India;Department of Civil Engineering, National Institute of Technology, Rourkela, India;Department of Civil Engineering, National Institute of Technology, Rourkela, India |
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| Abstract: | In the Himalayan regions, precipitation-runoff relationships are amongst the most complex hydrological phenomena, due to
varying topography and basin characteristics. In this study, different artificial neural networks (ANNs) algorithms were used to
simulate daily runoff at three discharge measuring sites in the Himalayan Kosi River Basin, India, using various combinations
of precipitation-runoff data as input variables. The data used for this study was collected for the monsoon period (June to October)
during the years of 2005 to 2009. ANNs were trained using different training algorithms, learning rates, length of data
and number of hidden neurons. A comprehensive multi-criteria validation test for precipitation-runoff modeling has been undertaken
to evaluate model performance and test its validity for generating scenarios. Global statistics have demonstrated that
the multilayer perceptron with three hidden layers (MLP-3) is the best ANN for basin comparisons with other MLP networks
and Radial Basis Functions (RBF). Furthermore, non-parametric tests also illustrate that the MLP-3 network is the best network
to reproduce the mean and variance of observed runoff. The performance of ANNs was demonstrated for flows during
the monsoon season, having different soil moisture conditions during period from June to October. |
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| Keywords: | precipitation-runoff ANN multilayer perceptron radial basis functions |
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