Modeling total resistance and form resistance of movable bed channels via experimental data and a kernel-based approach

Saghebian S. M., Roushangar K., Kirca V. Ş. Ö., Ghasempour R.

JOURNAL OF HYDROINFORMATICS, vol.22, no.3, pp.528-540, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 3
  • Publication Date: 2020
  • Doi Number: 10.2166/hydro.2020.094
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Environment Index, Pollution Abstracts
  • Page Numbers: pp.528-540
  • Keywords: bedform, experimental data, GPR, roughness coefficient, PREDICTION
  • Istanbul Technical University Affiliated: Yes


An accurate prediction of roughness coefficient in alluvial channels is of substantial importance for river management. In this study, the total and form resistance in alluvial channels with dune bedform were assessed using experimental data. First, the data of experiments carried out at the Hydraulic Laboratory of University of Tabriz was used to investigate the impact of hydraulic and sediment parameters on roughness coefficient. Then, these data were combined with other laboratory data, and the total and bedform resistance were modeled via a Gaussian Process Regression (GPR) approach. For models, developing different input combinations were considered based on flow and sediment characteristics. The obtained results from the experiments showed that the Reynolds number has a better correlation with flow resistance in comparison with other hydraulic parameters. It was found that the roughness variations due to bedform are almost between 40 and 80% of the total roughness coefficient. Also, the obtained results proved the capability of the GPR method in the modeling process. It was found that the model which took the advantages of both flow and sediment characteristics performed better compared to the other models. The sensitivity analysis results showed that the Reynolds number has the most significant impact in the prediction process.