Publication: Empirical Penman-Monteith equation and artificial intelligence techniques in predicting reference evapotranspiration: A review
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Date
2016
Authors
Abdullah S.S.
Malek M.A.
Journal Title
Journal ISSN
Volume Title
Publisher
Inderscience Publishers
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. � 2016 Inderscience Enterprises Ltd.
Description
Agricultural robots; Evapotranspiration; Neural networks; Water supply; Weather forecasting; Artificial intelligence techniques; Extreme learning machine; Fao penman monteiths; Food and agriculture organizations; Irrigation projects; Penman-Monteith equations; Reference evapotranspiration; Weather parameters; Learning systems; artificial intelligence; artificial neural network; climate conditions; empirical analysis; evapotranspiration; Food and Agricultural Organization; machine learning; Penman-Monteith equation; prediction; United Nations