Abstract
Abstract In recent years, the prediction of hydrological processes for the sustainable use of water resources has been a focus of research by scientists in the field of hydrology and water resources. Therefore, in this study, the prediction of daily streamflow using the artificial neural network (ANN), wavelet neural network (WNN) and adaptive neuro-fuzzy inference system (ANFIS) models were taken into account to develop the efficiency and accuracy of the models' performances, compare their results and explain their outcomes for future study or use in hydrological processes. To validate the performance of the models, 70% (1996–2007) of the data were used to train them and 30% (2008–2011) of the data were used to test them. The estimated results of the models were evaluated by the root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe (NS), and RMSE-observation standard deviation ratio (RSR) evaluation indexes. Although the outcomes of the models were comparable, the WNN model with RMSE = 0.700, R2 = 0.971, NS = 0.927, and RSR = 0.270 demonstrated the best performance compared to the ANN and ANFIS models.
Highlights
In recent years, streamflow prediction has been considered to be one of the most important issues in the fields of hydrology, water resources, and water resources management
Many studies have achieved successful predictions with the wavelet neural network (WNN) model, which is formed by combining wavelet analysis and artificial neural network (ANN) usage
This study developed and applied three different models to asses the efficiency and accuracy of each model in relation to the original data
Summary
Streamflow prediction has been considered to be one of the most important issues in the fields of hydrology, water resources, and water resources management. An accurate streamflow estimation can play a positive role in enhancing the capacity of reservoirs, flood prevention, water supply, design of hydroelectric projects, and water resources management It can have a significant impact on reducing the effects of climatic events on the environment and improve the efficiency of the outcomes. In the past few years, data-driven models, including artificial neural networks (ANNs), wavelet neural networks (WNNs), and adaptive neuro-fuzzy inference systems (ANFIS) models, have been applied as effective tools for. To estimate a hydrologic time series comprising nonlinear relationships, the use of data pre-processing techniques is needed to improve the performance of ANNs (Okkan ) One of these methods is the wavelet transform, which is a signal processing technique. & Burn ( ), Partal ( ) and Kisi ( ) used the streamflow values of the preceding days as an input parameter to estimate the streamflow of the Aegean
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