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Stream Flow Simulation using SVM, ANFIS and NAM Models (A Case Study)

Abstract

Stream flow forecasting can be an appropriate indicator in estimating future conditions for water resources management. The present study aimed to compare the efficiency of Support Vector Machine (SVM), Adaptive Neural Fuzzy Inference Systems (ANFIS) and conceptual hydrological model of MIKE11/NAM in simulating the daily stream flow. The studied area is Eskandari basin located in Iran. For this purpose, a ten-year period (1999-2009) of daily data including rainfall, runoff, temperature and evaporation were used. Furthermore, the performances of the models in flow simulation were investigated using statistical indicators of correlation coefficient (R2), Root Mean Square Error (RMSE) and the Nash-Sutcliffe (NS) coefficient. The results showed that every three models possess an appropriate performance and efficiency in the studied area. During testing (verification) period, SVM with the highest correlation coefficient (R2=0.99) and lowest RMSE equal to (RMSE=2.13 m3/s ), had a better performance than ANFIS model (R2=0.82, RMSE=3.21 m3/s ) and NAM model (R2=0.75, RMSE=3.48 m3/s ). In addition, Nash-Sutcliffe coefficient for SVM, ANFIS and NAM models were 0.99, 0.79 and 0.70, respectively.

Key words

Stream Flow Simulation; SVM; ANFIS; MIKE11/NAM Model

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