Journal
WATER
Volume 14, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/w14091325
Keywords
grain velocity; sediment transportation; shear velocity; ANN; SVM
Categories
Funding
- ICAR
- Portuguese Foundation for Science and Technology (FCT) [PTDC/CTA-OHR/30561/2017]
- Fundação para a Ciência e a Tecnologia [PTDC/CTA-OHR/30561/2017] Funding Source: FCT
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This study models grain velocity using soft computing approaches and evaluates the performance using quantitative indices. The results show that the SVM model provides more accurate predictions during the testing phase.
This study was undertaken with the primary objective of modeling grain velocity based on experimental data obtained under the controlled conditions of a laboratory using a rectangular hydraulic tilting channel. Soft computing approaches, i.e., support vector machine (SVM), artificial neural network (ANN), and multiple linear regression (MLR), were applied to simulate grain velocity using four input variables; shear velocity, exposed area to base area ratio (EATBAR), relative depth, and sediment particle weight. Quantitative performance evaluation of predicted values was performed with the help of three different standard statistical indices, such as the root mean square error (RMSE), Pearson's correlation coefficient (PCC), and Wilmot index (WI). The results during the testing phase revealed that the SVM model has RMSE (m/s), PCC, and WI values obtained as 0.1195, 0.8877, and 0.7243, respectively, providing more accurate predictions than the MLR and ANN models during the testing phase.
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