Daily inflow forecasting for Dukan reservoir in Iraq using artificial neural networks Online publication date: Thu, 30-Apr-2015
by Rafa H. Al-Suhili; Rizgar A. Karim
International Journal of Water (IJW), Vol. 9, No. 2, 2015
Abstract: Five ANN model versions were developed for the daily inflow forecasting to Dukan reservoir in Iraq. These models are dependent on the preceding days' lags (1, 2, 3, 4, and 5), respectively. The model versions forecasting correlation coefficients were found to be (94.6%, 94.6%, 95.2%, 95%, and 73%), respectively. The third model was used for forecasting and found capable of forecasting long term daily inflow series of the Dukan reservoir. Moreover it was found also capable of preserving the high and low persistences of this series in addition to the perfect simulation of the recession part and time to peak of the hydrograph.
Online publication date: Thu, 30-Apr-2015
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Water (IJW):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org