Prediction of Scour Depth below River Pipeline using Support Vector Machine
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
http://link.springer.com/content/pdf/10.1007/s12205-019-1327-0.pdf
Reference40 articles.
1. Ali, A., Sharma, R. K., Ganesan, P., and Akib, S. (2014). “Turbulence model sensitivity and scour gap effect of unsteady flow around pipe: A CFD study.” The Scientific World Journal, Vol. 11, DOI: https://doi.org/10.1155/2014/412136 .
2. Azamathulla, H. M. and Ahmad, Z. (2015). “Closure to “estimation of critical velocity for slurry transport through pipeline using Adaptive Neuro-Fuzzy Interference System and gene-expression programming” by H. Md. Azamathulla and Z. Ahmad.” Journal of Pipeline Systems Engineering and Practice, Vol. 6, DOI: https://doi.org/10.1061/(asce)ps.1949-1204.0000192 .
3. Azamathulla, H. M. and Ghani, A. A. (2010). “Genetic programming to predict river pipeline scour.” Journal of Pipeline Systems Engineering and Practice, Vol. 1, No. 3, pp. 127–132, DOI: https://doi.org/10.1061/(asce)ps.1949-1204.0000060 .
4. Azamathulla, H. M., Haghiabi, A. H., and Parsaie, A. (2016). “Prediction of side weir discharge coefficient by support vector machine technique.” Water Science and Technology: Water Supply, Vol. 16, No. 4, pp. 1002–1016, DOI: https://doi.org/10.2166/ws.2016.014 .
5. Azamathulla, H. M. and Yusoff, M. A. M. (2013). “Soft computing for prediction of river pipeline scour depth.” Neural Comput. & Applic., Vol. 23, No. 7, pp. 2465–2469, DOI: https://doi.org/10.1007/s00521-012-1205-x .
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