Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration

Shafika Sultan Abdullah, Marlinda Abdul Malek, Namiq Sultan Abdullah, A. Mustapha

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1053-1059
Number of pages7
JournalSains Malaysiana
Volume44
Issue number7
Publication statusPublished - 01 Jan 2015

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genetic algorithm
evapotranspiration
semiarid region
arid region
water
air temperature
Penman-Monteith equation
Food and Agricultural Organization
prediction
relative humidity
wind velocity
water resource
irrigation
weather
station
modeling
simulation

All Science Journal Classification (ASJC) codes

  • General

Cite this

Abdullah, Shafika Sultan ; Abdul Malek, Marlinda ; Abdullah, Namiq Sultan ; Mustapha, A. / Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration. In: Sains Malaysiana. 2015 ; Vol. 44, No. 7. pp. 1053-1059.
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Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration. / Abdullah, Shafika Sultan; Abdul Malek, Marlinda; Abdullah, Namiq Sultan; Mustapha, A.

In: Sains Malaysiana, Vol. 44, No. 7, 01.01.2015, p. 1053-1059.

Research output: Contribution to journalArticle

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