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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2013

01.08.2013 | Original Article

Multilayer perceptron for reference evapotranspiration estimation in a semiarid region

verfasst von: Hossein Tabari, P. Hosseinzadeh Talaee

Erschienen in: Neural Computing and Applications | Ausgabe 2/2013

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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.
Vorheriger Artikel Estimation of pressuremeter modulus and limit pressure of clayey soils by various artificial neural network models
Nächster Artikel Evaluation of effect of blast design parameters on flyrock using artificial neural networks

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Metadaten
Titel
Multilayer perceptron for reference evapotranspiration estimation in a semiarid region
verfasst von
Hossein Tabari
P. Hosseinzadeh Talaee
Publikationsdatum
01.08.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0904-7

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