Application of Acoustic Tomographic Data in Short-Term Forecasting of Streamflow Using Combinatorial GMDH Algorithm (CGA)

Document Type : Research Article


1 MSC Student, Iran University of Science and Technology / Faculty of Civil Engineering

2 Professor, Iran University of Science and Technology / Faculty of Civil Engineering

3 Assistant Professor, Water Research Institute

4 Assistant Professor, Iran University of Science and Technology / Faculty of Civil Engineering


Short-term forecasting of streamflow is one of the most important goals in water resources management and flood control. However, one of the problems that researchers always face in this type of prediction is the Lack of an accurate and high-resolution database. The Fluvial Acoustic Tomography (FAT) is an innovative technology that acquires streamflow data. Therefore, by using the data collected from this technology with a suitable forecast model, accurate short-term streamflow forecasting can be achieved. In this research, the effect of FAT data on short-term streamflow forecasting by the Combinatorial GMDH Algorithm (CGA) has been investigated and compared with one obtained from the Rating Curve method. The k-fold cross-validation criterion has been used to prevent over-fitting. The results showed that the FAT data increases the accuracy of short-term forecasting. As an example, the Nash-Sutcliffe coefficient (ENS) for the 1, 6, 12, 24, 48, and 72 hours forecast horizons were 0.98, 0.96, 0.94, 0.88, 0.73, and 0.54, respectively. While these values for the Rating Curve ones were 0.97, 0.84, 0.61, 0.27, 0.12, and 0.11, respectively.


Main Subjects

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