Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling
暂无分享,去创建一个
[1] H. Azamathulla,et al. Group method of data handling to predict scour depth around bridge piers , 2013, Neural Computing and Applications.
[2] Mohammad Najafzadeh,et al. Discussion of “Genetic Programming to Predict River Pipeline Scour” by H. Md. Azamathulla and Aminuddin Ab Ghani , 2013 .
[3] Mohammad Najafzadeh,et al. Abutment scour in clear-water and live-bed conditions by GMDH network. , 2013, Water science and technology : a journal of the International Association on Water Pollution Research.
[4] Aytac Guven,et al. Gene-expression programming for flip-bucket spillway scour. , 2012, Water science and technology : a journal of the International Association on Water Pollution Research.
[5] H. Md. Azamathulla,et al. Predicting wave-induced scour around a circular pile , 2012 .
[6] Mohammad Najafzadeh,et al. Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers , 2011 .
[7] Elena Toth,et al. Prediction of local scour depth at bridge piers under clear-water and live-bed conditions: comparison of literature formulae and artificial neural networks , 2011 .
[8] H. Md. Azamathulla,et al. Predictive model-based for the critical submergence of horizontal intakes in open channel flows with different clearance bottoms using CART, ANN and linear regression approaches , 2011, Expert Syst. Appl..
[9] H. Md. Azamathulla,et al. Linear genetic programming to scour below submerged pipeline , 2011 .
[10] Nor Azazi Zakaria,et al. Prediction of scour below submerged pipeline crossing a river using ANN. , 2011, Water science and technology : a journal of the International Association on Water Pollution Research.
[11] M. H. Kazeminezhad,et al. Prediction of wave-induced scour depth under submarine pipelines using machine learning approach , 2011 .
[12] Özgür Kisi,et al. A machine code-based genetic programming for suspended sediment concentration estimation , 2010, Adv. Eng. Softw..
[13] H. Md. Azamathulla,et al. Genetic Programming to Predict Bridge Pier Scour , 2010 .
[14] Koustuv Debnath,et al. Laboratory experiments on local scour around cylinder for clay and clay–sand mixed beds , 2010 .
[15] H. Md. Azamathulla,et al. Linear genetic programming for prediction of circular pile scour , 2009 .
[16] Aytac Guven,et al. Linear genetic programming for time-series modelling of daily flow rate , 2009 .
[17] Bernt J. Leira,et al. Scour below pipelines and around vertical piles due to second-order random waves plus a current , 2009 .
[18] Ali Moeini,et al. Investigating the efficiency in oil futures market based on GMDH approach , 2009, Expert Syst. Appl..
[19] F. Kalantary,et al. An investigation on the Su–NSPT correlation using GMDH type neural networks and genetic algorithms , 2009 .
[20] Dipti Srinivasan,et al. Energy demand prediction using GMDH networks , 2008, Neurocomputing.
[21] Hafzullah Aksoy,et al. Genetic Programming‐Based Empirical Model for Daily Reference Evapotranspiration Estimation , 2008 .
[22] Nader Nariman-zadeh,et al. Modelling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks , 2008 .
[23] Godfrey C. Onwubolu,et al. Design of hybrid differential evolution and group method of data handling networks for modeling and prediction , 2008, Inf. Sci..
[24] H. Md. Azamathulla,et al. Alternative neural networks to estimate the scour below spillways , 2008, Adv. Eng. Softw..
[25] Subhasish Dey,et al. Clear-Water Scour below Underwater Pipelines under Steady Flow , 2008 .
[26] Mustafa Gunal,et al. Genetic Programming Approach for Prediction of Local Scour Downstream of Hydraulic Structures , 2008 .
[27] Subhasish Dey,et al. Clear-water scour depth below underwater pipelines , 2007 .
[28] William Miller,et al. Live-Bed Local Pier Scour Experiments , 2006 .
[29] Józef Korbicz,et al. A GMDH neural network-based approach to robust fault diagnosis : Application to the DAMADICS benchmark problem , 2006 .
[30] P. B. Deolalikar,et al. Neural Networks for Estimation of Scour Downstream of a Ski-Jump Bucket , 2005 .
[31] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[32] Dag Myrhaug,et al. Scour below pipelines and around vertical piles in random waves , 2003 .
[33] Nader Nariman-zadeh,et al. Hybrid genetic design of GMDH-type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process , 2003 .
[34] Colin Campbell,et al. Kernel methods: a survey of current techniques , 2002, Neurocomputing.
[35] Toru Yamamoto,et al. A GMDH network using backpropagation and its application to a controller design , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.
[36] A. T. Moncada-M.,et al. SCOUR BELOW PIPELINE IN RIVER CROSSINGS , 2000 .
[37] B. Brørs. Numerical Modeling of Flow and Scour at Pipelines , 1999 .
[38] Fangjun Li,et al. Numerical Model for Local Scour under Offshore Pipelines , 1999 .
[39] B. Melville,et al. Scale Effect in Pier-Scour Experiments , 1998 .
[40] Takanori Shibata,et al. Genetic Algorithms And Fuzzy Logic Systems Soft Computing Perspectives , 1997 .
[41] Peggy A. Johnson,et al. Reliability‐Based Pier Scour Engineering , 1992 .
[42] Yee-Meng Chiew,et al. Prediction of Maximum Scour Depth at Submarine Pipelines , 1991 .
[43] Yee-Meng Chiew,et al. Mechanics of local scour around submarine pipelines , 1990 .
[44] Stanley J. Farlow,et al. Self-Organizing Methods in Modeling: Gmdh Type Algorithms , 1984 .
[45] Rkw Chee,et al. LIVE-BED SCOUR AT BRIDGE PIERS , 1982 .
[46] Robert Ettema,et al. SCOUR AT BRIDGE PIERS , 1980 .
[47] S P Kjeldsen,et al. LOCAL SCOUR NEAR OFFSHORE PIPELINES , 1973 .
[48] A. G. Ivakhnenko,et al. Polynomial Theory of Complex Systems , 1971, IEEE Trans. Syst. Man Cybern..
[49] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[50] Peter J. Angeline,et al. Extending Genetic Programming with Recombinative Guidance , 1996 .