Document Type : Research Paper

Authors

1 PhD Scholar, Department of Watershed Management Science and Engineering, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural and Natural Resources. Gorgan, Iran

2 Assist. Professor, Department of Rangeland and Watershed, Faculty of Natural Resources, Gonbadekavous University, Gonbadekavous, Iran

Abstract

In water resources management, there is a critical need to the prediction of the amount of inflow into the water supply system in order to be aware of future conditions and planning for optimal allocation of water resources to different sectors such as drinking, agriculture and. The aim of this study is to forecasting the monthly inflow to the Gorgan dam for future. To this aim, the data of the Qazaghli station with a 47-years history period and three Time series, neural network and Support vector machine models used for prediction. According to the obtained results, the ARIMA (1, 0, 0) (1, 0, 1) was found to be the premier parsimonious time series model based on the Akaike and Schwarz criteria. Moreover, The ANN model with 2 input and 10 neurons tuning and the SVM model with one input were the best performing models. Finally, according to the obtained results and evaluation criteria, the SVM model has the best efficacy in comparison with two other methods. The RMSE and AARE was 5.31 and 1.07 for SVM model, respectively; 9.88 and 2.78 for neural network, respectively and 8.84 and 1.07 has been obtained for Time Series model, respectively. Based on the results of this research, the best model to predict the monthly discharge input to the Gorgan dam was SVM.

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Abdollah Pour Azad M. R. and Sattari M. T. (2015). Forecasting daily river flow of Ahar Chay River using artificial neural networks (ANN) and comparison with adaptive neuro fuzzy inference system (ANFIS). J. Water Soil Conserv., 22(1), 287-298 [In Persian].
 
Ahmadi F., Radmanesh F. and Mirabbasi Najaf Abadi R. (2014). Comparison between genetic programming and support vector machine methods for daily river flow forecasting (case study: Barandoozchay River). J. Water Soil., 28(6), 1162-1171 [In Persian].
 
Alborzi M. (2001). Introduction to neural network. Sharif scientific publications, Tehran, Iran. 138pp. [In Persian].
 
Choubin B., Darabi H., Rahmati O., SajediHosseini F. and Kløve B. (2018). River suspended sediment modelling using the CART model: a comparative study of machine learning techniques. Sci. Total Environ., 615, 272-281.
 
Choubin B. and Malekian A. (2017). Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions. Environ. Earth Sci., 76, 538.
 
Choubin B., Zehtabian G., Azareh A., RafieiSardooi E. and Sajedi-Hosseini F. (2018). Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches. Environ. Earth Sci., 77, 314.
 
Hornik K., Stinchcombe M., White H. (1989). Multilayer feedforward networks are universal approximators. Neural Net., 2(5), 359-366.
 
Jalal Kamali N. (2002). Monthly flow prediction into the Jiroft reservoir Dam by the theory of time series.  Proc. 2004 2nd Congress of International River Engineering, Shahid Chamran University, Ahvaz, Iran [In Persian].
 
Karamouz M. and Araghinejd Sh. (2005). Advanced hydrology, Amirkabir University of Technology Press, Tehran, Iran. 464pp. [In Persian]. 
 
Karimi Masouleh, H., Ahmadvand, M., Moazed, H., 2010. Neural networks application in Karun river flow prediction based on upstream stations rainfall data during the past six months. Proc. 2010 8th Congress of International River Engineering, Shahid Chamran University, Ahvaz, Iran, [In Persian].
 
Kisi O. (2004). River flow modeling using artificial neural networks. J. Hudrol. Eng., 9(1), 26-38.
 
Nanduri U. V. and Swain P. C. (2005). Streamflow forecasting using nuero-fuzzy inference system. AGU Fall Meeting.
 
Rezaei A. (2004). Regional modeling of peak discharges in sub-watershed of Sefidroud Dam using artificial neural network. PhD Thesis, Tehran University, Tehran, Iran [In Persian]. 
 
Salamatian S. dehghani A. and Aghdasian M. (2006). Smart estimating of river discharge using neural network. Proc. 2006 1ed regional conference of optimum utilization of Karoon and Zayandehrud Basins water resources. University of Shahrekord, Shahrekord, Iran [In Persian]. 
 
Salarijazi M., Ghorbani K., Sohrabian E. and Abdolhosseini M. (2016). Prediction of daily stream-flow using data driven models. Iran. J. Irrig. Drain., 4(10), 479-488 [In Persian].
 
Chen S. T., Yu P. S. (2007). Real-time probabilistic forecasting of flood stages. J. Hydrol., 340, 63-77 Valipour M., Banihabib M. E. and Behbahani S. M. R. (2013). Comparsion of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez Dam River. J. Hydrol., 476, 433-441.