Prediction of equilibrium scour depth in uniform non-cohesive sediments downstream of an apron using computational intelligence
| Autor: | Abbas Rezaei, Mitra Javan, Mohsen Hayati, Afshin Eghbalzadeh |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Hydrology
Adaptive neuro fuzzy inference system Engineering Environmental Engineering Artificial neural network business.industry Computer Science::Neural and Evolutionary Computation 0208 environmental biotechnology Computational intelligence 02 engineering and technology Perceptron Tailwater 020801 environmental engineering Hydraulic structure Multilayer perceptron 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Geotechnical engineering Sensitivity (control systems) business Civil and Structural Engineering |
| Zdroj: | European Journal of Environmental and Civil Engineering. 22:28-41 |
| ISSN: | 2116-7214 1964-8189 |
| DOI: | 10.1080/19648189.2016.1179677 |
| Popis: | Accurate prediction of equilibrium scour depth downstream of hydraulic structures has an important role in their appropriate design. In this paper, the applicability of artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models in prediction of equilibrium scour depth is studied. Multi-layer perceptron neural network structure is employed for the training of the ANN models. Experimental data of published literature are used for training and testing the models. The equilibrium scour depth is predicted as a function of five input variables; apron length, sluice gate opening, issuing velocity of jet, tailwater depth and median sediment size. The comparison of results shows that ANN model provides a better prediction of scour depth than ANFIS as well as the previous empirical relationship obtained by regression analysis. However, the results indicate that both ANN and ANFIS models can successfully predict equilibrium scour depth. Finally, based on the sensitivity analysis, it w... |
| Databáze: | OpenAIRE |
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