Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir

Abrahart, 2001, Investigating the role of saliency analysis with a neural network rainfall–runoff model, Comput. Geosci., 27, 921, 10.1016/S0098-3004(00)00131-X

Avarideh, F., 2012. Application of Hydro-Informatics in Sediment Transport, Ph.D. Thesis, Amirkabir University of Technology, Tehran, Iran.

Baareh, 2006, Forecasting river flow in the USA: a comparison between auto-regression and neural network non-parametric models, J. Comput. Sci., 2, 775, 10.3844/jcssp.2006.775.780

Balaguer, 2008, Predicting service request in support centers based on nonlinear dynamics, ARMA modeling and neural networks, Expert Syst. Appl., 34, 665, 10.1016/j.eswa.2006.10.003

Banihabib, M.E., Mousavi, S.M., Jamali, F.S., 2008. Artificial neural network model to study the spatial and temporal correlation between stations in reservoir inflow forecasting. In: 3rd Iran Water Resources Management Conference, Tabriz, Iran.

Box, 1976

Chegini, A.G., 2012. MATLAB Tools, Naghous Press. <http://www.naghoospress.ir/bookview.aspx?bookid=1485875>.

Chiang, 2007, Dynamic ANN for precipitation estimation and forecasting from radar observations, J. Hydrol., 334, 250, 10.1016/j.jhydrol.2006.10.021

Coulibaly, 2000, Daily reservoir inflow forecasting using artificial neural networks with stopped training approach, J. Hydrol., 230, 244, 10.1016/S0022-1694(00)00214-6

Cryer, 2008, 10.1007/978-0-387-75959-3

Heidarnejad, 2012, Modeling suspended sediments in Dez basin (case study: the Tale Zang hydrometric station), Int. Res. J. Appl. Basic Sci., 3, 402

Karamouz, 2012

Karunasinghe, 2006, Chaotic time series prediction with a global model: artificial neural network, J. Hydrol., 3232, 92, 10.1016/j.jhydrol.2005.07.048

Kisi, I., Cigizoglu, K., 2005. Reservoir management using artificial neural networks. In: 14th. Reg. Directorate of DSI (State Hydraulic Works), Istanbul, Turkey.

Menhaj, 2012

Mohammadi, 2005, Comparison of regression ARIMA and ANN models for reservoir inflow forecasting using snowmelt equivalent (a case study of Karaj), J. Agric. Sci. Technol., 7, 17

Pulido-Calvo, 2007, Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds, J. Hydrol., 332, 1, 10.1016/j.jhydrol.2006.06.015

Sadrolashrafi, S.S., Samadi, A., Rodzi, M.A., Thamer, A.M., 2008. Flood modeling using WMS Software: A case study of the Dez River Basin, Iran, River flow 2008-Altinakar, Kokpinar, Darama, Yegen & Harmancioglu (Eds.) © 2008 Kubaba Congress Department and Travel Services ISBN 978-605-60136-3-8.

Sahoo, 2006, Use of neural network to predict flash flood and attendant water qualities of a mountainous stream on Oahu, Hawaii, J. Hydrol., 327, 525, 10.1016/j.jhydrol.2005.11.059

Teschl, 2006, A neural network model for short term river flow prediction, Nat. Hazards Earth Syst. Sci., 6, 29, 10.5194/nhess-6-629-2006

Toth, 2000, Comparison of short-term rainfall predication models for real-time flood forecasting, J. Hydrol., 239, 132, 10.1016/S0022-1694(00)00344-9

Valipour, 2012, Critical areas of Iran for agriculture water management according to the annual rainfall, Eur. J. Sci. Res., 84, 600

Valipour, 2012, Number of required observation data for rainfall forecasting according to the climate conditions, Am. J. Sci. Res., 74, 79

Wei, 1990