A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions

Fereshteh Modaresi (), Shahab Araghinejad () and Kumars Ebrahimi ()
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Fereshteh Modaresi: University of Tehran
Shahab Araghinejad: University of Tehran
Kumars Ebrahimi: University of Tehran

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 1, No 14, 243-258

Abstract: Abstract Monthly forecasting of streamflow is of particular importance in water resources management especially in the provision of rule curves for dams. In this paper, the performance of four data-driven models with different structures including Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN), Least Square-Support Vector Regression (LS-SVR), and K-Nearest Neighbor Regression (KNN) are evaluated in order to forecast monthly inflow to Karkheh dam, Iran, in linear and non-linear conditions while the optimized values of the model parameters are determined in the same condition via the Leave-One-Out Cross Validation (LOOCV) method. Results show that the performance of the models is different in linear and nonlinear conditions; the cumulative ranking of the models according to the three assessment criteria including NSE, RMSE and R2 indicates that ANN performs best in linear conditions while LS-SVR, GRNN and KNN are in the next ranks, respectively. But in nonlinear conditions, the best performance belongs to LS-SVR, followed by KNN, ANN, and GRNN models.

Keywords: Comparative assessment; Cumulative ranking; Karkheh; Leave-One-Out Cross Validation (LOOCV); Linear and Nonlinear conditions (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (14)

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DOI: 10.1007/s11269-017-1807-2

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