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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 150-156 (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: Storage levels prediction ability in dam reservoirs is a critical issue within the dam management system and protection from flood, depending on the values of precipitation and runoff coming into the reservoir over different periods of time. Objectives: water level in the 16th Dam reservoir on the North Kebir River in Syria, using artificial neural networks using (ANNs). Methods: The daily measured water level 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 water level in the dam reservoir, 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 water level in 16th Tishreen dam reservoir where the (1-10-4) feed forward neural network provides a high predictability of water levels dam of the next day, especially during the rainy months.
Keywords: Feed forward, Back Propagation, water level, reservoir.
Background: Storage levels prediction ability in dam reservoirs is a critical issue within the dam management system and protection from flood, depending on the values of precipitation and runoff coming into the reservoir over different periods of time. Objectives: water level in the 16th Dam reservoir on the North Kebir River in Syria, using artificial neural networks using (ANNs). Methods: The daily measured water level 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 water level in the dam reservoir, 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 water level in 16th Tishreen dam reservoir where the (1-10-4) feed forward neural network provides a high predictability of water levels dam of the next day, especially during the rainy months.
Keywords: Feed forward, Back Propagation, water level, reservoir.
*Correspondant author and authors Copyright © 2017:
| Ghatfan, Abdalkareem Ammar 1 | Badia, Yousef Haidar * 2 | and | Alaa, Ali Slieman 3 |
| Ghatfan, Abdalkareem Ammar 1 | Badia, Yousef Haidar * 2 | and | Alaa, Ali Slieman 3 |
Authors Contact
Affiliation.
1. Tishreen University | department of water engineering and irrigation | Lattakia | Syria |
2. Tishreen University | department of structural engineering | Lattakia | Syria |
3. Tishreen University | department of water engineering and irrigation | Lattakia | Syria |
1. Tishreen University | department of water engineering and irrigation | Lattakia | Syria |
2. Tishreen University | department of structural engineering | Lattakia | Syria |
3. Tishreen University | department of water engineering and irrigation | Lattakia | Syria |
This article is made freely available as part of this journal's Open Access: ID | Haidar-ManuscriptRef.1-ajira190417 |
| 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
WATER LEVEL PREDICTION IN 16TH TISHREEN DAM RESERVOIR USING ARTIFICIAL NEURAL NETWORKS
| Ghatfan, Abdalkareem Ammar | and | Badia, Yousef Haidar |. Am. J. innov. res. appl. sci. 2017; 4(5):150-156.
| PDF FULL TEXT | |Received | 19 April 2017| |Accepted | 26 April 2017| |Published 02 May 2017 |
WATER LEVEL PREDICTION IN 16TH TISHREEN DAM RESERVOIR USING ARTIFICIAL NEURAL NETWORKS
| Ghatfan, Abdalkareem Ammar | and | Badia, Yousef Haidar |. Am. J. innov. res. appl. sci. 2017; 4(5):150-156.
| PDF FULL TEXT | |Received | 19 April 2017| |Accepted | 26 April 2017| |Published 02 May 2017 |