Closure to “Prediction of River Pipeline Scour Depth Using Multivariate Adaptive Regression Splines” by Amir Hamzeh Haghiabi
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Volume 10, Issue 2
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References
Azamathulla, H. M., and A. A. Ghani. 2010. “Genetic programming to predict river pipeline scour.” J. Pipeline Syst. Eng. Pract. 1 (3): 127–132. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000060.
Azamathulla, H. M., and M. A. Mohd. Yusoff. 2013. “Soft computing for prediction of river pipeline scour depth.” Neural Comput. Appl. 23 (7–8): 2465–2469. https://doi.org/10.1007/s00521-012-1205-x.
Etemad-Shahidi, A., R. Yasa, and M. H. Kazeminezhad. 2011. “Prediction of wave-induced scour depth under submarine pipelines using machine learning approach.” Appl. Ocean Res. 33 (1): 54–59. https://doi.org/10.1016/j.apor.2010.11.002.
Najafzadeh, M., G.-A. Barani, and H. M. Azamathulla. 2014. “Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling.” Neural Comput. Appl. 24 (3–4): 629–635. https://doi.org/10.1007/s00521-012-1258-x.
Noori, R., Z. Deng, A. Kiaghadi, and F. T. Kachoosangi. 2016. “How reliable are ANN, ANFIS, and SVM techniques for predicting longitudinal dispersion coefficient in natural rivers.” J. Hydraul. Eng. 142 (1): 04015039. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001062.
Parsaie, A., H. M. Azamathulla, and A. H. Haghiabi. 2017. “Prediction of discharge coefficient of cylindrical weir–gate using GMDH-PSO.” ISH J. Hydraul. Eng. 24 (2): 116–123. https://doi.org/10.1080/09715010.2017.1372226.
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Published In
Journal of Pipeline Systems Engineering and Practice
Volume 10 • Issue 2 • May 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Nov 23, 2017
Accepted: Oct 5, 2018
Published online: Feb 8, 2019
Published in print: May 1, 2019
Discussion open until: Jul 8, 2019
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