No 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.

Damage caused by floods in some parts of the world, especially in Asia and the Pacific, accounted for the highest rate among the damage resulting from other natural disasters such as landslides, earthquake and tsunamis. Due to this factor, has motivated us to study further on flood forecasting. In previous studies, researchers focus on separate three criteria which are reliability groups, time complexity and error rate to forecast flood. In this paper, we study and analyze the three mentioned criteria in order to identify the highest criteria utilized in flood forecasting. The number of references studied and analyzed is in the year 2010 until 2019. From our findings, the highest criteria identified are under the reliability group, with highest accuracy index of 90%.