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

    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.

,

Document Type : Research Paper

10.22059/jrwm.2016.61699
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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
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Journal of Range and Watershed Managment
Volume 69, Issue 2
July 2016
Pages 503-514
Files
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  • PDF 2.85 M
History
  • Receive Date: 11 June 2016
  • Revise Date: 01 August 2016
  • Accept Date: 24 May 2017
  • First Publish Date: 24 May 2017
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How to cite
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  • BibTeX
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Statistics
  • Article View: 530
  • PDF Download: 387

APA

Nassaji zavareh, M., Ghermezcheshmeh, B., & Rahimzadeh, F. (2016). Reconstruction of Daily Discharge using Artificial Neural Network and Neuro-Fuzzy Methods (Case Study: Upstream of Karoun Watershed). Journal of Range and Watershed Managment, 69(2), 503-514. doi: 10.22059/jrwm.2016.61699

MLA

Mojtaba Nassaji zavareh; Bagher Ghermezcheshmeh; Fatemeh Rahimzadeh. "Reconstruction of Daily Discharge using Artificial Neural Network and Neuro-Fuzzy Methods (Case Study: Upstream of Karoun Watershed)". Journal of Range and Watershed Managment, 69, 2, 2016, 503-514. doi: 10.22059/jrwm.2016.61699

HARVARD

Nassaji zavareh, M., Ghermezcheshmeh, B., Rahimzadeh, F. (2016). 'Reconstruction of Daily Discharge using Artificial Neural Network and Neuro-Fuzzy Methods (Case Study: Upstream of Karoun Watershed)', Journal of Range and Watershed Managment, 69(2), pp. 503-514. doi: 10.22059/jrwm.2016.61699

VANCOUVER

Nassaji zavareh, M., Ghermezcheshmeh, B., Rahimzadeh, F. Reconstruction of Daily Discharge using Artificial Neural Network and Neuro-Fuzzy Methods (Case Study: Upstream of Karoun Watershed). Journal of Range and Watershed Managment, 2016; 69(2): 503-514. doi: 10.22059/jrwm.2016.61699

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