Journal of Range and Watershed Managment

Reconstruction of Daily Discharge using Artificial Neural Network and Neuro-Fuzzy Methods (Case Study: Upstream of Karoun Watershed)

Document Type : Research Paper

Authors

Mojtaba Nassaji zavareh 1
Bagher Ghermezcheshmeh 2
Fatemeh Rahimzadeh 3

1 Assistant Professor, Institute of Technical & Vocational Higher Education Agriculture Jihad, Agricultural Research, Education and Extension Organization (AREEO), Tehran, I.R.IRAN.

2 Assistant professor, Soil Conservation and Watershed Management Research Institute, AREEO, Tehran, I.R.IRAN.

3 Faculty Member of Atmospheric Science and Meteorological Research Center, I.R.IRAN.

https://doi.org/10.22059/jrwm.2016.61699
Abstract
Daily constant discharges are needed estimating daily discharge in the hydrological model. The different number of statistical years, statistical deficiencies, and measurement error leads to the formation of time series with an uncommon time base. Hence the reconstruction of daily discharge data is of paramount importance. In this research, daily discharge was reconstructed in two stages in one of the upstream of Karoun River. In both stages of research, daily discharge data from two upstream stations were used to reconstruct daily discharge of the downstream station using artificial neural networks, neuro-fuzzy and two variables regression methods. In the second stage, the magnitudes of discharge, based on dry, normal and wet years was used to reconstruct the daily discharge. The results showed higher accuracy in the artificial neural network and neuro-fuzzy methods compared to two variable regression methods in the reconstruction of daily discharge. Multi-layer perceptron model has better potential among all different method of artificial neural network and neuro-fuzzy models. Classification of discharge into dry, normal, and wet years decreases error in the reconstruction of daily discharge. Based on the mean relative error (MRE), error in reconstruction of daily discharge is the least in normal, wet, and dry years, respectively

Keywords

Reconstruction
daily discharge
Artificial Neural Network
neuro-fuzzy
Karoun
Journal of Range and Watershed Managment
Volume 69, Issue 2
Summer 2016
Pages 503-514

  • Receive Date 11 June 2016
  • Revise Date 01 August 2016
  • Accept Date 24 May 2017
  • Publish Date 21 June 2016

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