Volume 11 Issue 4
Oct.  2018
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Article Navigation >  Water Science and Engineering  > 2018  >  11(4): 328-337
Hossein Kheirfam, Sahar Mokarram-Kashtiban. 2018: A regional suspended load yield estimation model for ungauged watersheds. Water Science and Engineering, 11(4): 328-337. doi: 10.1016/j.wse.2018.09.008
Citation: Hossein Kheirfam, Sahar Mokarram-Kashtiban. 2018: A regional suspended load yield estimation model for ungauged watersheds. Water Science and Engineering, 11(4): 328-337. doi: 10.1016/j.wse.2018.09.008
shu

A regional suspended load yield estimation model for ungauged watersheds

doi: 10.1016/j.wse.2018.09.008

  • a Department of Environmental Science, Urmia Lake Research Institute, Urmia University, Urmia 57561-51818, Iran

  • b Department of Forestry, Tarbiat Modares University, Noor 46417-76489, Iran

Funds:  This work was supported by the Department of Environmental Science,Urmia Lake Research Institute, Urmia University.
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  • Corresponding author: Hossein Kheirfam
  • Received Date: 2018-04-22
  • Rev Recd Date: 2018-09-11
    Abstract
  • Developing regional models using physiographic, climatic, and hydrologic variables is an approach to estimating suspended load yield (SLY) in ungauged watersheds. However, using all the variables might reduce the applicability of these models. Therefore, data reduction techniques (DRTs), e.g., principal component analysis (PCA), Gamma test (GT), and stepwise regression (SR), have been used to select the most effective variables. The artificial neural network (ANN) and multiple linear regression (MLR) are also common tools for SLY modeling. We conducted this study (1) to obtain the most effective variables influencing SLY through DRTs including PCA, GT, and SR, and then, to use them as input data for ANN and MLR; and (2) to provide the best SLY models. Accordingly, we used 14 physiographic, climatic, and hydrologic parameters from 42 watersheds in the Hyrcanian forest region (in northern Iran). The most effective variables as determined through DRTs as well as the original data sets were used as the input data for ANN and MLR in order to provide an SLY model. The results indicated that the SLY models provided by ANN performed much better than the MLR models, and the GT-ANN model was the best. The determination of coefficient, relative error, root mean square error, and bias were 99.9%, 26%, 323 t/year, and 6 t/year in the calibration period, and 70%, 43%, 456 t/year, and 407 t/year in the validation period, respectively. Overall, selecting the main factors that influce SLY and using artificial intelligence tools can be useful for water resources managers to quickly determine the behavior of SLY in ungauged watersheds.

     

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