Application of Acoustic tomographic data to the short-term streamflow forecasting using data-driven methods and discrete wavelet transform

Abstract

High-accurate streamflow data obtained from the Fluvial Acoustic Tomography (FAT) increase forecasting accuracy, especially in the case of short-term forecasting. The reason is due to the better temporal resolution and higher measurement frequency than the traditional Rating-Curve (RC) method. In this study, Multilayer GMDH Algorithm (MGA) and Combinatorial GMDH Algorithm (CGA) were used to forecast the short-term streamflow. These models, as well as their combinations with the discrete wavelet transform (DWT), were applied to the 6-month streamflow datasets obtained from the FAT and RC method, and the hourly forecasts of streamflow were done with 1 to 48 h ahead lead times. The results showed that the MGA model has a better performance than the CGA model. It was also observed that combining of the MGA model with DWT increased the forecasting accuracy. However, based on the concept of Occam’s razor and due to the insignificant difference between MGA and DWT-MGA models, the simpler one, i.e., MGA, was chosen as the optimal model. The comparison of forecasting results using the FAT and RC revealed that in the case of applying FAT dataset, the Nash-Sutcliffe (NSE) rate ranged from 0.99 to 0.73 for 1-hour ahead to 48-hour ahead forecasts, whereas, it adversely changed from 0.98 to 0.06 for the RC dataset. This study demonstrates the capability of FAT data in providing reliable and accurate hourly streamflow forecasting.