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94971

ESTIMATING OF EVAPOTRANSPIRATION USING ARTIFICIAL NEURAL NETWORK

Article

Last updated: 26 Dec 2024

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Agricultural Irrigation and Drainage Engineering

Abstract

This study investigates the application of artificial neural networks (ANNs) on the prediction of daily grass reference crop evapotranspiration (ET0) and compares the performance of ANNs with the conventional method (Penman–Monteith). The use of ANNs was examined number of hidden layers and the activation function were also tested. The best ANN architecture for estimation of daily ET0 was obtained for different data set for Nubaria. Using these data, the networks were trained with daily climatic data (maximum and minimum temperature, dew point and wind speed) as input and the Penman– Monteith (PM) estimated ET0 as output. The analysis was carried out with MATLAB software. Feed forward one-layer networks with sigmoid function were used. Performance evaluation of the models have been carried out by calculating root mean square error (RMSE), The network were selected based on maximized R and R2 value and minimized RMSE values which were 0.98, 0.957 and 0.44 mm/day, respectively in testing. The optimal ANN (4-12-1) for Nubaria regions showed a satisfactory performance in the ET0 estimation. These ANN models may therefore be adopted for estimating ET0 in the study area with reasonable degree of accuracy.

DOI

10.21608/mjae.2020.94971

Keywords

reference evapotranspiration, Penman-Monteith Method, artificial neural network, Back- propagation Algorithm, Training, Testing

Authors

First Name

M. A.

Last Name

Genaidy

MiddleName

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Affiliation

Agric. Eng. Dept., Fac. of Agric., Ain Shams Univ., Egypt.

Email

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City

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Orcid

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Volume

37

Article Issue

1

Related Issue

14338

Issue Date

2020-01-01

Receive Date

2020-06-11

Publish Date

2020-01-01

Page Start

81

Page End

94

Print ISSN

1687-384X

Online ISSN

2636-3062

Article File

MJAE_Volume 37_Issue 1_Pages 81-94.pdf

PDF . 933.5kB

Link

https://mjae.journals.ekb.eg/article_94971.html

Detail API

https://mjae.journals.ekb.eg/service?article_code=94971

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5

Type

Original Article

Type Code

1,326

Publication Type

Journal

Publication Title

Misr Journal of Agricultural Engineering

Publication Link

https://mjae.journals.ekb.eg/

MainTitle

ESTIMATING OF EVAPOTRANSPIRATION USING ARTIFICIAL NEURAL NETWORK

Details

Type

Article

Created At

23 Jan 2023