Abstract
The reliable forecasting of river flow plays a key role in reducing the risk of floods. Regarding nonlinear and variable characteristics of hydraulic processes, the use of data-driven and hybrid methods has become more noticeable. Thus, this paper proposes a novel hybrid wavelet-neural network (WNN) method with feature extraction to forecast river flow. To do this, initially, the collected data are analyzed by the wavelet method. Then, the number of inputs to the ANN is determined using feature extraction, which is based on energy, standard deviation, and maximum values of the analyzed data. The proposed method has been analyzed by different input and various structures for daily, weekly, and monthly flow forecasting at Ellen Brook river station, western Australia. Furthermore, the mean squares error (MSE), root mean square error (RMSE), and the Nash-Sutcliffe efficiency (NSE) is used to evaluate the performance of the suggested method. Furthermore, the obtained findings were compared to those of other models and methods in order to examine the performance and efficiency of the feature extraction process. It was discovered that the proposed feature extraction model outperformed their counterparts, especially when it came to long-term forecasting.
Highlights
Water demand is increasing daily due to population growth, irrigation, and industrial developments
By using this method, feature extraction that reduces the volume of neural network input data increase the accuracy of forecasting and even reduce the processing and training time. It makes it possible to use the properties of all data in a certain range, unlike references that consider only several previous days. This proposed method focuses on improving accuracy and reducing the risk of river flow forecasting (Ellen Brook River, western Australia) by presenting the new waveletneural network (WNN) model and applying it to three daily, weekly, and monthly time scales
Are as follows: (a) input data are used in two groups for network train and testing; (b) by the me input data are used in two groups for network train and testing; (b) by fulfilling fulfilling the mentioned conditions after applying the appropriate transfer coefficients tioned conditions after applying the appropriate transfer coefficients and using a prop and using a proper the mother is transformed into anwavelet; offspring scale,scale, the mother waveletwavelet is transformed into an offspring (c) wavelet; activation function (c) activation functions in thelayer hidden layerofneurons of the neural replaced in the hidden neurons the neural network arenetwork replacedare with different types with different types of offspring (d) the established
Summary
Water demand is increasing daily due to population growth, irrigation, and industrial developments. A new combined method for forecasting river flow is presented based on neural networks and wavelet transform While this type of hybrid method has been used in previous research, but in this paper, an intermediate step is considered to reduce the complexity and increase the accuracy. It makes it possible to use the properties of all data in a certain range, unlike references that consider only several previous days This proposed method focuses on improving accuracy and reducing the risk of river flow forecasting (Ellen Brook River, western Australia) by presenting the new WNN model and applying it to three daily, weekly, and monthly time scales.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
