Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models

Rana Muhammad Adnan, Xiaohui Yuan, Ozgur Kisi, Yanbin Yuan


This paper investigates the ability of two soft computing methods including artificial neural network (ANN) and support vector machine (SVM) models in modeling monthly streamflow. The results of ANN and SVM models are compared on basis of determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) to evaluate the performance of the applied models. Comparison of results indicates that the SVM models with RMSE = 147.01 m3/s, MAE = 86.68 m3/s and R2 = 0.872 in test period is superior in forecasting monthly streamflows than the ANN models with RMSE = 161.59 m3/s, MAE = 94.87 m3/s and R2 = 0.869, respectively. It is found that SVM models can be successfully used in predicting monthly streamflows.


Streamflow; Soft computing models; ANN; SVM.

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