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American Journal of Innovative Research & Applied Sciences
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| | ARTICLES | Am. J. innov. res. appl. sci. Volume 4, Issue 5, Pages 157-162 (May 2017) |
American Journal of innovative
Research & Applied Sciences
Research & Applied Sciences
ISSN 2429-5396 (Online)
OCLC Number: 920041286
OCLC Number: 920041286
|
| MAY | VOLUME 4 | N° 5 | 2017 |
ABSTRACT
Background: Evapotranspiration is an important component of the hydrological cycle, and the accurate estimation of this parameter is very important for many water resources applications. Objectives: The objective of this study was to estimate monthly reference evapotranspiration (ET0) at Homs meteostation in Syria using artificial neural networks (ANNs) with minimal measured climate data such as the air temperature (maximum, minimum and average) used in this paper. Methods: The monthly reference evapotranspiration data were estimated by the Penman Monteith method, which is the sole standard method, and used as output of the neural networks. Results: The results of this study showed that feed forward back propagation Artificial Neural Networks (FFBP-ANNs) estimated successfully the monthly ET0 using air temperature data, with low values of root mean square errors (RMSE), and high values of correlation coefficients (R). The result also showed that the using of the monthly index improves the accurate of estimation with correlation coefficient (R) of 99.7 %, and root mean square error (RMSE) of 7.28 mm/month for the test period. Conclusions: Thus, this research has shown the high reliability of artificial neural networks in estimation of monthly reference evapotranspiration at Homs meteostation using the air temperature data, and we can use these models in other places by adding the air temperature data in these places to the Homs meteostation’s dataset and retraining the network again.
Keywords: Feedforward, Back Propagation, Penman Monteith, Monthly Index.
Background: Evapotranspiration is an important component of the hydrological cycle, and the accurate estimation of this parameter is very important for many water resources applications. Objectives: The objective of this study was to estimate monthly reference evapotranspiration (ET0) at Homs meteostation in Syria using artificial neural networks (ANNs) with minimal measured climate data such as the air temperature (maximum, minimum and average) used in this paper. Methods: The monthly reference evapotranspiration data were estimated by the Penman Monteith method, which is the sole standard method, and used as output of the neural networks. Results: The results of this study showed that feed forward back propagation Artificial Neural Networks (FFBP-ANNs) estimated successfully the monthly ET0 using air temperature data, with low values of root mean square errors (RMSE), and high values of correlation coefficients (R). The result also showed that the using of the monthly index improves the accurate of estimation with correlation coefficient (R) of 99.7 %, and root mean square error (RMSE) of 7.28 mm/month for the test period. Conclusions: Thus, this research has shown the high reliability of artificial neural networks in estimation of monthly reference evapotranspiration at Homs meteostation using the air temperature data, and we can use these models in other places by adding the air temperature data in these places to the Homs meteostation’s dataset and retraining the network again.
Keywords: Feedforward, Back Propagation, Penman Monteith, Monthly Index.
*Correspondant author and authors Copyright © 2017:
| Ghatfan, Abdalkareem Ammar * 1 | and | Badia, Yousef Haidar 2 |
| Ghatfan, Abdalkareem Ammar * 1 | and | Badia, Yousef Haidar 2 |
Authors Contact
Affiliation.
1. Tishreen University | Department of water engineering and irrigation | Lattakia | Syria |
2. Tishreen University | Department of structural engineering | Lattakia | Syria |
1. Tishreen University | Department of water engineering and irrigation | Lattakia | Syria |
2. Tishreen University | Department of structural engineering | Lattakia | Syria |
This article is made freely available as part of this journal's Open Access: ID | Badia ManuscriptRef.1-ajiras210816|
| ISSN: 2429-5396 (e) | www.american-jiras.com | |
| Web Site Form: v 0.1.05 | JF 22 Cours, Wellington le Clairval, Lillebonne | France |
| Web Site Form: v 0.1.05 | JF 22 Cours, Wellington le Clairval, Lillebonne | France |
Research Article
ESTIMATION OF REFERENCE EVAPOTRANSPIRATION BASED ON ONLY TEMPERATURE DATA USING ARTIFICIAL NEURAL NETWORK
| Ghatfan, Abdalkareem Ammar | Badia, Yousef Haidar | and | Alaa, Ali Slieman |. Am. J. innov. res. appl. sci. 2017; 4(5):157-162.
| PDF FULL TEXT | |Received | 22 August 2016| |Accepted | 25 April 2017| |Published 02 May 2017 |
ESTIMATION OF REFERENCE EVAPOTRANSPIRATION BASED ON ONLY TEMPERATURE DATA USING ARTIFICIAL NEURAL NETWORK
| Ghatfan, Abdalkareem Ammar | Badia, Yousef Haidar | and | Alaa, Ali Slieman |. Am. J. innov. res. appl. sci. 2017; 4(5):157-162.
| PDF FULL TEXT | |Received | 22 August 2016| |Accepted | 25 April 2017| |Published 02 May 2017 |