Title: Empirical Penman-Monteith equation and artificial intelligence techniques in predicting reference evapotranspiration: a review

Authors: Shafika Sultan Abdullah; Marlinda Abdul Malek

Addresses: Department of Civil Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia; Akre Technical Institute, Dohuk polytechnic University, Dohuk, Iraq ' The Institute of Energy, Policy and Research (IEPRe), Universiti Tenaga Nasional, Malaysia

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.

Keywords: FAO Penman-Monteith equation; artificial neural networks; ANNs; extreme learning machines; ELM; evapotranspiration; irrigation projects; weather parameters; simulation; nonlinear processes; dynamic processes.

DOI: 10.1504/IJW.2016.073741

International Journal of Water, 2016 Vol.10 No.1, pp.55 - 66

Received: 04 Feb 2014
Accepted: 16 Jun 2014

Published online: 17 Dec 2015 *

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