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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 16^th 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 16^th 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.