Daily inflow forecasting for Dukan reservoir in Iraq using artificial neural networks

Autor:	Rafa H. Al-Suhili, Rizgar A. Karim
Rok vydání:	2015
Předmět:	Meteorology Artificial neural network Series (mathematics) Climatology Geography Planning and Development Environmental science Time to peak Hydrograph Inflow Management Monitoring Policy and Law Time series Water Science and Technology Term (time)
Zdroj:	International Journal of Water. 9:194
ISSN:	1741-5322 1465-6620
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f240398c85b513aaa68d22ee4164c483 https://doi.org/10.1504/ijw.2015.068961 Zobrazit plný text záznamu