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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.
Keywords
ARTIFICIAL NEURAL-NETWORKS, PAN EVAPORATION, EQUATIONS, PERFORMANCE, MODELS, Reference evapotranspiration, Artificial neural network, Learning algorithm, Meteorological parameters, Iran

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MLA
Tabari, Hossein, and Parisa Hosseinzadehtalaei. “Multilayer Perceptron for Reference Evapotranspiration Estimation in a Semiarid Region.” NEURAL COMPUTING & APPLICATIONS, vol. 23, no. 2, 2013, pp. 341–48, doi:10.1007/s00521-012-0904-7.
APA
Tabari, H., & Hosseinzadehtalaei, P. (2013). Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. NEURAL COMPUTING & APPLICATIONS, 23(2), 341–348. https://doi.org/10.1007/s00521-012-0904-7
Chicago author-date
Tabari, Hossein, and Parisa Hosseinzadehtalaei. 2013. “Multilayer Perceptron for Reference Evapotranspiration Estimation in a Semiarid Region.” NEURAL COMPUTING & APPLICATIONS 23 (2): 341–48. https://doi.org/10.1007/s00521-012-0904-7.
Chicago author-date (all authors)
Tabari, Hossein, and Parisa Hosseinzadehtalaei. 2013. “Multilayer Perceptron for Reference Evapotranspiration Estimation in a Semiarid Region.” NEURAL COMPUTING & APPLICATIONS 23 (2): 341–348. doi:10.1007/s00521-012-0904-7.
Vancouver
1.
Tabari H, Hosseinzadehtalaei P. Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. NEURAL COMPUTING & APPLICATIONS. 2013;23(2):341–8.
IEEE
[1]
H. Tabari and P. Hosseinzadehtalaei, “Multilayer perceptron for reference evapotranspiration estimation in a semiarid region,” NEURAL COMPUTING & APPLICATIONS, vol. 23, no. 2, pp. 341–348, 2013.
@article{01HV696PTCHRDZEX1ZBS5ZT9EC,
  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.}},
  author       = {{Tabari, Hossein and Hosseinzadehtalaei, Parisa}},
  issn         = {{0941-0643}},
  journal      = {{NEURAL COMPUTING & APPLICATIONS}},
  keywords     = {{ARTIFICIAL NEURAL-NETWORKS,PAN EVAPORATION,EQUATIONS,PERFORMANCE,MODELS,Reference evapotranspiration,Artificial neural network,Learning algorithm,Meteorological parameters,Iran}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{341--348}},
  title        = {{Multilayer perceptron for reference evapotranspiration estimation in a semiarid region}},
  url          = {{http://doi.org/10.1007/s00521-012-0904-7}},
  volume       = {{23}},
  year         = {{2013}},
}

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