Empirical Penman-Monteith equation and artificial intelligence techniques in predicting reference evapotranspiration

A review

Shafika Sultan Abdullah, Marlinda Abdul Malek

Research output: Contribution to journalReview article

3 Citations (Scopus)

Abstract

Evapotranspiration is a fundamental requirement in the planning and management of irrigation projects. Methods of predicting evapotranspiration (ET) are numerous, but the Food and Agriculture Organization (FAO) of the United Nations adopted the FAO Penman-Monteith (PM) equation, as the method which provides the most accurate results for the prediction of reference evapotranspiration (ET0) in all regions and for all weather conditions. The main identified drawback in the application of this method is the wide variety of weather parameters required for the prediction. To overcome this problem, artificial neural networks (ANNs) models have been proposed to simulate the nonlinear, dynamic ET0 processes. This paper highlights both the traditional empirical PM method, and the enhancement obtained by the utilisation of ANN techniques in predicting ET0.

Original languageEnglish
Pages (from-to)55-66
Number of pages12
JournalInternational Journal of Water
Volume10
Issue number1
DOIs
Publication statusPublished - 01 Jan 2016

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Penman-Monteith equation
artificial intelligence
evapotranspiration
neural network
artificial neural network
agriculture
food
organization
prediction
United Nations
irrigation
UNO
utilization
weather
planning
method
management

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Water Science and Technology
  • Management, Monitoring, Policy and Law

Cite this

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abstract = "Evapotranspiration is a fundamental requirement in the planning and management of irrigation projects. Methods of predicting evapotranspiration (ET) are numerous, but the Food and Agriculture Organization (FAO) of the United Nations adopted the FAO Penman-Monteith (PM) equation, as the method which provides the most accurate results for the prediction of reference evapotranspiration (ET0) in all regions and for all weather conditions. The main identified drawback in the application of this method is the wide variety of weather parameters required for the prediction. To overcome this problem, artificial neural networks (ANNs) models have been proposed to simulate the nonlinear, dynamic ET0 processes. This paper highlights both the traditional empirical PM method, and the enhancement obtained by the utilisation of ANN techniques in predicting ET0.",
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Empirical Penman-Monteith equation and artificial intelligence techniques in predicting reference evapotranspiration : A review. / Abdullah, Shafika Sultan; Abdul Malek, Marlinda.

In: International Journal of Water, Vol. 10, No. 1, 01.01.2016, p. 55-66.

Research output: Contribution to journalReview article

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