Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration

Water scarcity is a global concern, as the demand for water is increasing tremendously and poor management of water resources will accelerates dramatically the depletion of available water. The precise prediction of evapotranspiration (ET), that consumes almost 100% of the supplied irrigation water,...

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Main Authors:	Shafika Sultan Abdullah, M.A., Malek, Namiq Sultan Abdullah, A., Mustapha
Format:	Article
Language:	English
Published:	Universiti Kebangsaan Malaysia 2015
Online Access:	http://journalarticle.ukm.my/8991/ http://journalarticle.ukm.my/8991/ http://journalarticle.ukm.my/8991/1/18_Shafika_Sultan.pdf

id	ukm-8991
recordtype	eprints
spelling	ukm-89912016-12-14T06:48:39Z http://journalarticle.ukm.my/8991/ Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration Shafika Sultan Abdullah, M.A., Malek Namiq Sultan Abdullah, A., Mustapha Water scarcity is a global concern, as the demand for water is increasing tremendously and poor management of water resources will accelerates dramatically the depletion of available water. The precise prediction of evapotranspiration (ET), that consumes almost 100% of the supplied irrigation water, is one of the goals that should be adopted in order to avoid more squandering of water especially in arid and semiarid regions. The capabilities of feedforward backpropagation neural networks (FFBP) in predicting reference evapotranspiration (ET0) are evaluated in this paper in comparison with the empirical FAO Penman-Monteith (P-M) equation, later a model of FFBP+Genetic Algorithm (GA) is implemented for the same evaluation purpose. The study location is the main station in Iraq, namely Baghdad Station. Records of weather variables from the related meteorological station, including monthly mean records of maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine hours (Rn), relative humidity (Rh) and wind speed (U2), from the related meteorological station are used in the prediction of ET0 values. The performance of both simulation models were evaluated using statistical coefficients such as the root of mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The results of both models are promising, however the hybrid model shows higher efficiency in predicting ET0 and could be recommended for modeling of ET0 in arid and semiarid regions. Universiti Kebangsaan Malaysia 2015-07 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/8991/1/18_Shafika_Sultan.pdf Shafika Sultan Abdullah, and M.A., Malek and Namiq Sultan Abdullah, and A., Mustapha (2015) Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration. Sains Malaysiana, 44 (7). pp. 1053-1059. ISSN 0126-6039 http://www.ukm.my/jsm/malay_journals/jilid44bil7_2015/KandunganJilid44Bil7_2015.html
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	Water scarcity is a global concern, as the demand for water is increasing tremendously and poor management of water resources will accelerates dramatically the depletion of available water. The precise prediction of evapotranspiration (ET), that consumes almost 100% of the supplied irrigation water, is one of the goals that should be adopted in order to avoid more squandering of water especially in arid and semiarid regions. The capabilities of feedforward backpropagation neural networks (FFBP) in predicting reference evapotranspiration (ET0) are evaluated in this paper in comparison with the empirical FAO Penman-Monteith (P-M) equation, later a model of FFBP+Genetic Algorithm (GA) is implemented for the same evaluation purpose. The study location is the main station in Iraq, namely Baghdad Station. Records of weather variables from the related meteorological station, including monthly mean records of maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine hours (Rn), relative humidity (Rh) and wind speed (U2), from the related meteorological station are used in the prediction of ET0 values. The performance of both simulation models were evaluated using statistical coefficients such as the root of mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The results of both models are promising, however the hybrid model shows higher efficiency in predicting ET0 and could be recommended for modeling of ET0 in arid and semiarid regions.
format	Article
author	Shafika Sultan Abdullah, M.A., Malek Namiq Sultan Abdullah, A., Mustapha
spellingShingle	Shafika Sultan Abdullah, M.A., Malek Namiq Sultan Abdullah, A., Mustapha Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration
author_facet	Shafika Sultan Abdullah, M.A., Malek Namiq Sultan Abdullah, A., Mustapha
author_sort	Shafika Sultan Abdullah,
title	Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration
title_short	Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration
title_full	Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration
title_fullStr	Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration
title_full_unstemmed	Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration
title_sort	feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration
publisher	Universiti Kebangsaan Malaysia
publishDate	2015
url	http://journalarticle.ukm.my/8991/ http://journalarticle.ukm.my/8991/ http://journalarticle.ukm.my/8991/1/18_Shafika_Sultan.pdf
first_indexed	2023-09-18T19:53:42Z
last_indexed	2023-09-18T19:53:42Z
_version_	1777406388845150208

Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration

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