349
Views
13
CrossRef citations to date
0
Altmetric
Articles

Pier scour modelling using random forest regression

, &
Pages 69-75 | Received 23 Dec 2012, Accepted 01 Feb 2013, Published online: 28 Feb 2013

References

  • Azmathullah , H.M. , Deo , M.C. and Deolalikar , P.B. 2005 . Neural networks for estimation of scour downstream of a ski-jump bucket . Journal of Hydraulic Engineering , 131 ( 10 ) : 898 – 908 .
  • Azmathullah , H.M. , Deo , M.C. and Deolalikar , P.B. 2006 . Estimation of scour below spillway using neural networks . Journal of Hydraulic Research , 44 ( 1 ) : 61 – 69 .
  • Bateni , S.M. , Borghei , S.M. and Jeng , D.-S. 2007 . Neural network and neuro-fuzzy assessments for scour depth around bridge piers . Engineering Applications of Artificial Intelligence , 20 : 401 – 414 .
  • Bhattacharya, B and Solomatine, D.P. (2003). “Neural networks and M5 model trees in modelling water level-discharge relationship for an Indian river”. In European Symposium on Artificial Neural Networks, Bruges (Belgium), 23–25 April, 407–412.
  • Bochkanov, S and Bystritsky, V. (2012), “ALGLIB, www.alglib.net”. (July 5, 2012).
  • Bogdan , T. , Magdalena , S. , Zbigniew , T. and Tadeusz , L. 2012 . Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms . International Journal of Applied Mathematics and Computer Science , 22 ( 4 ) : 867 – 881 .
  • Breiman , L. 1996 . Bagging predictors . Machine Learning , 24 ( 2 ) : 123 – 140 .
  • Breiman, L. (1999). “Random forests - Random Features.” Technical Report 567, Statistics Department, University of California, Berkeley, ftp://ftp.stat.berkeley.edu/pub/users/breiman
  • Breiman , L. , Friedman , J.H. , Olshen , R.A. and Stone , C.J. 1984 . Classification and Regression Trees , Monterey , CA : Wadsworth .
  • Feller , W. 1968 . “An Introduction to Probability Theory and Its Application”. Vol. 1 , 3rd edition , New York : Wiley .
  • Froehlich, D.C. (1988). “Analysis of onsite measurements of scour at piers.” National Conference on Hydraulic Engineering. American Society of Civil Engineers, Colorado Springs, CO, 534–539.
  • Herrera , M. , Torgo , L. , Izquierdo , J. and Pérez-García , R. 2010 . Predictive Models for Forecasting Hourly Urban Water Demand . Journal of Hydrology , 387 ( 1-2 ) : 141 – 150 .
  • Jain , P. , Deo , M.C. , Latha , G. and Rajendran , V. 2011 . Real time wave forecasting using wind time history and numerical model . Ocean Modelling , 36 ( 1-2 ) : 26 – 39 .
  • Jones, J.S. (1984). “Comparison of prediction equations for bridge pier and abutment scour”. Trans. Res. Rec. 1950, Transportation Research Board, Washington.
  • Kambekar , A.R. and Deo , M.C. 2003 . Estimation of group pile scour using neural networks . Applied Ocean Research , 25 ( 4 ) : 225 – 234 .
  • Kandasamy , J.K. and Melville , B.W. 1998 . Maximum local scour depth at bridge piers and abutments . Journal of Hydraulic Research , 36 ( 2 ) : 183 – 198 .
  • Kaya , A. 2010 . Artificial neural network study of observed pattern of scour depth around bridge piers . Computers and Geotechnics , 37 ( 3 ) : 413 – 418 .
  • Kothyari , U.C. , Garde , R.J. and RangaRaju , K.G. 1992 . Temporal variation of scour around circular bridge piers . Journal of Hydraulic Engineering , 118 ( 8 ) : 1091 – 1106 .
  • Kusiak , A. , Zheng , H. and Zhang , Z. 2011 . Virtual Wind Speed Sensor for Wind Turbines . Journal of Energy Engineering , 137 ( 2 ) : 59 – 69 .
  • Lariviere , B. and Vandenpoel , D.V. 2005 . Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques . Expert Systems with Applications , 29 ( 2 ) : 472 – 484 .
  • Lee , T.L. , Jeng , D.S. , Zhang , G.H. and Hong , J.H. 2007 . Neural network modeling for estimation of scour depth around bridge piers . Journal of Hydrodynamics , 19 : 378 – 386 .
  • Leshem , G. and Ritov , Y. 2007 . Traffic Flow Prediction using Adaboost Algorithm with Random Forests as a Weak Learner . Proceedings of World Academy of Science, Engineering and Technology , 21 : 193 – 198 .
  • Mueller, D. S and Wagner, C. R. (2005). “Field observations and evaluations of streambed scour at bridges”. Office of Engineering Research and Development, Federal Highway Administration, Report No. FHWA–RD–03–052, 134 pages.
  • Pal , M. and Deswal , S. 2009 . M5 Model Tree Based Modelling of Reference Evapotranspiration . Hydrologic Processes , 23 ( 10 ) : 1437 – 1443 .
  • Pal , M. and Mather , P.M. 2003 . An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification . Remote Sensing of Environment , 86 : 554 – 565 .
  • Pal , M. , Singh , N.K. and Tiwari , N.K. 2011 . Support Vector Regression based Modeling of Pier Scour using Field Data . Engineering Applications of Artificial Intelligence , 24 ( 5 ) : 911 – 916 .
  • Pal , M. , Singh , N.K. and Tiwari , N.K. 2012 . M5 model tree for pier scour prediction using field dataset . KSCE Journal of Civil Engineering , 16 ( 6 ) : 1079 – 1084 .
  • Palmer , D.P. , O'Boyle , N.M. , Glen , R.C. and Mitchell , J.B.O. 2007 . Random Forest Models to Predict Aqueous Solubility . Journal of Chemical Information and Modeling , 47 ( 1 ) : 150 – 158 .
  • Polishchuk , P.G. , Muratov , E.N. , Artemenko , A.G. , Kolumbin , O.G. , Muratov , N.N. and Kuzmin , V.E. 2009 . Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity . Journal of Chemical Information and Modeling , 49 ( 11 ) : 2481 – 2488 .
  • Quinlan, J. R. (1992). “Learning with continuous classes.” Proceedings of Australian Joint Conference on Artificial Intelligence, World Scientific Press: Singapore, 343–348.
  • Singh , K.K. , Pal , M. and Singh , V.P. 2010 . Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree . Water Resource Management , 24 ( 10 ) : 2007 – 2019 .
  • Solomatine D. P and Siek, M. B. (2004). “Flexible and optimal M5 model trees with applications to flow predictions”. Proceedings of 6th International Conference on Hydroinformatics (Edited by Liong, Phoon, Babovic), World Scientific Press: Singapore, 1719–1726.
  • Zounemat-Kermani , M. , Beheshti , A.-A. , Ataie-Ashtiani , B. and Sabbagh-Yazdi , S.-R. 2009 . Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system . Applied Soft Computing , 9 : 746 – 755 .

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.