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Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions

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Abstract

This study employed two artificial neural network (ANN) models, including multi-layer perceptron (MLP) and radial basis function (RBF), as data-driven methods of hourly air temperature at three meteorological stations in Fars province, Iran. MLP was optimized using the Levenberg–Marquardt (MLP_LM) training algorithm with a tangent sigmoid transfer function. Both time series (TS) and randomized (RZ) data were used for training and testing of ANNs. Daily maximum and minimum air temperatures (MM) and antecedent daily maximum and minimum air temperatures (AMM) constituted the input for ANNs. The ANN models were evaluated using the root mean square error (RMSE), the coefficient of determination (R 2) and the mean absolute error. The use of AMM led to a more accurate estimation of hourly temperature compared with the use of MM. The MLP-ANN seemed to have a higher estimation efficiency than the RBF ANN. Furthermore, the ANN testing using randomized data showed more accurate estimation. The RMSE values for MLP with RZ data using daily maximum and minimum air temperatures for testing phase were equal to 1.2°C, 1.8°C, and 1.7°C, respectively, at Arsanjan, Bajgah, and Kooshkak stations. The results of this study showed that hourly air temperature driven using ANNs (proposed models) had less error than the empirical equation.

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Acknowledgement

The data used to carry out this research were provided by the Islamic Republic of Iran Meteorological Office (IRIMO). The first author would like to especially thank Prof. Vijay P. Singh and Dr. E. Zia Hosseinipour for his inestimable helps and also Mr. Amin Bandegi for data preparation.

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Authors and Affiliations

  1. Young Researchers Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran

    Mehdi Rezaeian-Zadeh

  2. Irrigation Department, Agricultural College, Shiraz University, Shiraz, Iran

    Shahrookh Zand-Parsa

  3. Department of Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran

    Hirad Abghari

  4. School of Water Sciences Engineering, Chamran University of Ahwaz, Ahwaz, Iran

    Masih Zolghadr

  5. Department of Biological & Agricultural Engineering, Texas A&M University, College station, TX, 77843-2117, USA

    Vijay P. Singh

  6. Department of Civil and Environmental Engineering, Texas A&M University, College station, TX, 77843-2117, USA

    Vijay P. Singh

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  1. Mehdi Rezaeian-Zadeh
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  2. Shahrookh Zand-Parsa
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  3. Hirad Abghari
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  4. Masih Zolghadr
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  5. Vijay P. Singh
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Correspondence to Mehdi Rezaeian-Zadeh.

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Rezaeian-Zadeh, M., Zand-Parsa, S., Abghari, H. et al. Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions. Theor Appl Climatol 109, 519–528 (2012). https://doi.org/10.1007/s00704-012-0595-0

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  • DOI: https://doi.org/10.1007/s00704-012-0595-0

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