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Predicting Monthly Streamflow Using a Hybrid Wavelet Neural Network: Case Study of the Çoruh River Basin

Predicting Monthly Streamflow Using a Hybrid Wavelet Neural Network: Case Study of the Çoruh River Basin
Mehmet Şamil Güneş*, Coşkun Parim, Doğan Yıldız, Ali Hakan Büyüklü
Department of Statistics, Yildiz Technical University, Istanbul, Turkey
 
Abstract
In this study, a hybrid model combining discrete wavelet transforms (WTs) and artificial neural
networks (ANNs) is used to estimate the monthly streamflow. The WT-ANN hybrid model was developed
using the Daubechies main wavelet to predict the streamflow for three gauging stations on the Çoruh
river basin one month in advance, with different combinations of air temperature, precipitation, and
streamflow variables, and their wavelet transformations. Four different hybrid WT-ANN models were
generated and compared with four different conventional ANN models. The dataset was chronologically
divided into training, validation, and testing data. The results indicated that the WT-ANN hybrid
models performed better than the traditional ANN models for all three stations. Furthermore,
the chronologically divided dataset was used to examine the effects of changes in hydrological data
over time on model performance. In conclusion, model performances in the training period deteriorated
during the validation and testing periods due to structural changes in the hydrological data.


Keywords: streamflow, artificial neural network (ANN), wavelet transform (WT), air temperature,
precipitation

to read the full article please click on 

/uploads/editor/images/Predicting_Monthly_Streamflow_Using_a_Hy.pdf

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