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Multilayer perceptron for reference evapotranspiration estimation in a semiarid region

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Abstract

Calculation of reference evapotranspiration (ETo) is essential in hydrology and agriculture. ETo plays an important role in planning and management of water resources and irrigation scheduling. The results of many studies strongly support the use of the Penman–Monteith FAO 56 (PMF-56) method as the standard method of estimating ETo. The basic obstacle to using this method widely is the numerous meteorological variables required. Multilayer perceptron (MLP) networks optimized with different learning algorithms and activation functions were applied for estimating ETo in a semiarid region in Iran. Four MLP models comprising various combinations of meteorological variables are developed. The MLP model which needs all of the meteorological parameters performed best for ETo estimation amongst the other MLP models. It was also found that the ConjugateGradient, DeltaBarDelta, DeltaBarDelta and Levenberg–Marquardt were the best algorithms for training the MLP1, MLP2, MLP3 and MLP4 models, respectively.

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Acknowledgments

The authors would like to thank the Islamic Republic of Iran Meteorological Organization (IRIMO) for providing the requisite meteorological data.

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Authors and Affiliations

  1. Department of Water Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran

    Hossein Tabari

  2. Young Researchers Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran

    P. Hosseinzadeh Talaee

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  1. Hossein Tabari
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  2. P. Hosseinzadeh Talaee
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Correspondence to P. Hosseinzadeh Talaee.

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Tabari, H., Hosseinzadeh Talaee, P. Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Comput & Applic 23, 341–348 (2013). https://doi.org/10.1007/s00521-012-0904-7

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  • DOI: https://doi.org/10.1007/s00521-012-0904-7

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