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A self-tuning ANN model for simulation and forecasting of surface flows

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Abstract

Artificial neural networks (ANN) are applicable for and forecasting without the need to calculate complex nonlinear functions. This paper evaluates the effectiveness of temperature, evapotranspiration, precipitation and inflow factors, and the lag time of those factors, as variables for simulating and forecasting of runoff. The genetic algorithm (GA) is coupled with ANN to determine the optimal set of variables for streamflow forecasting. The minimization of the total mean square error (MSE) is considered as the objective function of the ANN-GA method in this paper. Our results show the effectiveness of the ANN-GA for simulating and forecasting runoff with consistent accuracy compared with using pure ANN for runoff simulation and forecasting.

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Authors and Affiliations

  1. Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran

    Omid Bozorg-Haddad

  2. Department of Water Resources Engineering, Tarbiat Modares University, Tehran, Iran

    Mahboubeh Zarezadeh-Mehrizi

  3. Faculty of Agricultural Engineering and Technology, Department of Irrigation and Reclamation, College of Agriculture and Natural Resources, University of Tehran, Karaj, Tehran, Iran

    Mehri Abdi-Dehkordi

  4. Department of Geography, University of California, Santa Barbara, CA, 93016-4060, USA

    Hugo A. Loáiciga

  5. Department of Land, Air & Water Resources, Department of Civil & Environmental Engineering, and Department of Biological & Agricultural Engineering, University of California, 139 Veihmeyer Hall, Davis, CA, 95616-8628, USA

    Miguel A. Mariño

Authors
  1. Omid Bozorg-Haddad
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  2. Mahboubeh Zarezadeh-Mehrizi
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  3. Mehri Abdi-Dehkordi
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  4. Hugo A. Loáiciga
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  5. Miguel A. Mariño
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Correspondence to Omid Bozorg-Haddad.

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Bozorg-Haddad, O., Zarezadeh-Mehrizi, M., Abdi-Dehkordi, M. et al. A self-tuning ANN model for simulation and forecasting of surface flows. Water Resour Manage 30, 2907–2929 (2016). https://doi.org/10.1007/s11269-016-1301-2

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  • DOI: https://doi.org/10.1007/s11269-016-1301-2

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