Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform

Authors:
Aman Mohammad Kalteh
Affiliations:
Department of Range and Watershed Management, Faculty of Natural Resources, University of Guilan, P.O. Box 1144, Sowmehe Sara, Guilan Province, Iran
Venue:
Computers & Geosciences
Year:
2013

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Abstract

Reliable and accurate forecasts of river flow is needed in many water resources planning, design development, operation and maintenance activities. In this study, the relative accuracy of artificial neural network (ANN) and support vector regression (SVR) models coupled with wavelet transform in monthly river flow forecasting is investigated, and compared to regular ANN and SVR models, respectively. The relative performance of regular ANN and SVR models is also compared to each other. For this, monthly river flow data of Kharjegil and Ponel stations in Northern Iran are used. The comparison of the results reveals that both ANN and SVR models coupled with wavelet transform, are able to provide more accurate forecasting results than the regular ANN and SVR models. However, it is found that SVR models coupled with wavelet transform provide better forecasting results than ANN models coupled with wavelet transform. The results also indicate that regular SVR models perform slightly better than regular ANN models.