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Influence of Time Discretization and Input Parameter on the ANN Based Synthetic Streamflow Generation

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Abstract

The capability of ANN to generate synthetic series of river discharge averaged over different time steps with limited data has been investigated in the present study. While an ANN model with certain input parameters can generate a monthly averaged streamflow series efficiently; it fails to generate a series of smaller time steps with the same accuracy. The scope of improving efficiency of ANN in generating synthetic streamflow by using different combinations of input data has been analyzed. The developed models have been assessed through their application in the river Subansiri in India. Efficiency of the ANN models has been evaluated by comparing ANN generated series with the historical series and the series generated by Thomas-Fiering model on the basis of three statistical parameters-periodical mean, periodical standard deviation and skewness of the series. The results reveal that the periodical mean of the series generated by both Thomas–Fiering and ANN models are in good agreement with that of the historical series. However, periodical standard deviation and skewness coefficient of the series generated by Thomas–Fiering model is inferior to that of the series generated by ANN.

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Abbreviations

p :

period which may be 10 days or month t year

q av,p :

mean of the historical streamflow series for period p(current period t)

q av,p+1 :

mean of the historical streamflow series for period p + 1(next period)

σ p :

standard deviation of historical series of period p

σ p+1 :

standard deviation of historical series of period p + 1 respectively

r p,p+1 :

correlation between period p and p + 1 of historical series

ξ p,t :

independent standard normal random variable

q p,t :

two dimensional array in which p represents period i.e. January, February,……, December while t represents for year in which t = 1 means for first year and so on.

q p+1,t :

logarithmic discharge which has generated for period p + 1int th year

y j (t) :

standardized target value for pattern j, y j  = output response from the network for pattern j

p :

total number of training pattern

q :

number of output nodes

α:

momentum factor

η:

learning rate

p n :

normalized input

p a :

actual input

min p :

minimum value of input vector

max p :

maximum value of the input vector

I t :

streamflow of current period

I t-1 :

streamflow of previous period

μt+1 :

mean of historical streamflow of next period

σ t+1 :

standard deviation of historical streamflow of next period

min t+1 :

minimum value of inflow from the given historical record

max t+1 :

maximum value of inflow from the given historical record

G t+1 :

average time rate of change of discharge of the series

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Authors and Affiliations

  1. Department of Civil Engineering, Galgotia College of Engineering & Technology, Greater Noida, 201306, Uttar Pradesh, India

    Maya Rajnarayan Ray

  2. Department of Civil Engineering, Indian Institute of Technology, Guwahati, India

    Arup Kumar Sarma Ph.D.

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  1. Maya Rajnarayan Ray
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  2. Arup Kumar Sarma Ph.D.
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Correspondence to Arup Kumar Sarma Ph.D..

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Ray, M.R., Sarma, A.K. Influence of Time Discretization and Input Parameter on the ANN Based Synthetic Streamflow Generation. Water Resour Manage 30, 4695–4711 (2016). https://doi.org/10.1007/s11269-016-1448-x

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