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Volumn 32, Issue 4, 2016, Pages 691-704

Extreme learning machine assessment for estimating sediment transport in open channels

Author keywords

Bed load; Extreme learning machines (ELM); Limit of deposition; Sediment transport; Storm water

Indexed keywords

BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; DEPOSITION; GENETIC ALGORITHMS; GENETIC PROGRAMMING; KNOWLEDGE ACQUISITION; SEDIMENT TRANSPORT; SEDIMENTATION; SEDIMENTS;

EID: 84962302725     PISSN: 01770667     EISSN: 14355663     Source Type: Journal    
DOI: 10.1007/s00366-016-0446-1     Document Type: Article
Times cited : (66)

References (44)
  • 1
  • 2
    • 77249104673 scopus 로고    scopus 로고
    • Non-deposition design criteria for sewers with part-full flow
    • Vongvisessomjai N, Tingsanchali T, Babel MS (2010) Non-deposition design criteria for sewers with part-full flow. Urban Water J 7(1):61–77. doi:10.1080/15730620903242824
    • (2010) Urban Water J , vol.7 , Issue.1 , pp. 61-77
    • Vongvisessomjai, N.1    Tingsanchali, T.2    Babel, M.S.3
  • 3
    • 84906664118 scopus 로고    scopus 로고
    • Verification of equation for non-deposition sediment transport in flood water canals
    • Lausanne: Switzerland
    • Bonakdari H, Ebtehaj I. (2014) Verification of equation for non-deposition sediment transport in flood water canals. In: 7th International Conference on Fluvial Hydraulics, RIVER FLOW 2014, Lausanne, Switzerland, 1527–1533. doi:10.1201/b17133-203
    • (2014) 7th International Conference on Fluvial Hydraulics, RIVER FLOW , vol.2014 , pp. 1527-1533
    • Bonakdari, H.1    Ebtehaj, I.2
  • 4
    • 0030015806 scopus 로고    scopus 로고
    • Design options for self-Cleansing storm sewers
    • Nalluri C, Ab Ghani A (1996) Design options for self-Cleansing storm sewers. Water Sci Technol 33(9):215–220. doi:10.1016/0273-1223(96)00389-7
    • (1996) Water Sci Technol , vol.33 , Issue.9 , pp. 215-220
    • Nalluri, C.1    Ab Ghani, A.2
  • 6
    • 0037387466 scopus 로고    scopus 로고
    • Urban storm sewer design: approach in consideration of sediments
    • Ota JJ, Nalluri C (2003) Urban storm sewer design: approach in consideration of sediments. J Hydraul Eng 129(4):291–297. doi:10.1061/(ASCE)0733-9429(2003)129:4(291)
    • (2003) J Hydraul Eng , vol.129 , Issue.4 , pp. 291-297
    • Ota, J.J.1    Nalluri, C.2
  • 7
    • 47349101623 scopus 로고    scopus 로고
    • Hydraulic performance of sewer pipes with deposited sediments
    • Banasiak R (2008) Hydraulic performance of sewer pipes with deposited sediments. Water Sci Technol 57(11):1743–1748. doi:10.2166/wst.2008.287
    • (2008) Water Sci Technol , vol.57 , Issue.11 , pp. 1743-1748
    • Banasiak, R.1
  • 8
    • 84919918148 scopus 로고    scopus 로고
    • Design criteria for sediment transport in sewers based on self-cleansing concept
    • Ebtehaj I, Bonakdari H, Sharifi A (2014) Design criteria for sediment transport in sewers based on self-cleansing concept. J Zhejiang Univ Sci-A 15(11):914–924. doi:10.1631/jzus.A1300135
    • (2014) J Zhejiang Univ Sci-A , vol.15 , Issue.11 , pp. 914-924
    • Ebtehaj, I.1    Bonakdari, H.2    Sharifi, A.3
  • 9
    • 26444480333 scopus 로고    scopus 로고
    • Neural networks for estimation of scour downstream of a ski-jump bucket
    • Azmathullah HMd, Deo MC, Deolalikar PB (2005) Neural networks for estimation of scour downstream of a ski-jump bucket. J Hydraul Eng 131(10):898–908. doi:10.1061/(ASCE)0733-9429(2005)131:10(898)
    • (2005) J Hydraul Eng , vol.131 , Issue.10 , pp. 898-908
    • Azmathullah, H.M.1    Deo, M.C.2    Deolalikar, P.B.3
  • 10
    • 33645813389 scopus 로고    scopus 로고
    • Estimation of scour below spillways using neural networks
    • Azmathullah HMd, Deo MC, Deolalikar PB (2006) Estimation of scour below spillways using neural networks. J Hydraul Res 44(1):61–69. doi:10.1080/00221686.2006.9521661
    • (2006) J Hydraul Res , vol.44 , Issue.1 , pp. 61-69
    • Azmathullah, H.M.1    Deo, M.C.2    Deolalikar, P.B.3
  • 11
    • 41549084200 scopus 로고    scopus 로고
    • Alternative neural networks to estimate the scour below spillways
    • Azmathullah HMd, Deo MC, Deolalikar PB (2008) Alternative neural networks to estimate the scour below spillways. Adv Eng Softw 39(8):689–698. doi:10.1016/j.advengsoft.2007.07.004
    • (2008) Adv Eng Softw , vol.39 , Issue.8 , pp. 689-698
    • Azmathullah, H.M.1    Deo, M.C.2    Deolalikar, P.B.3
  • 12
    • 84908606898 scopus 로고    scopus 로고
    • Performance evaluation of two different neural network and particle swarm optimization methods for prediction of discharge capacity of modified triangular side weirs
    • Zaji AH, Bonakdari H (2014) Performance evaluation of two different neural network and particle swarm optimization methods for prediction of discharge capacity of modified triangular side weirs. Flow Meas Instrum 40:149–156. doi:10.1016/j.flowmeasinst.2014.10.002
    • (2014) Flow Meas Instrum , vol.40 , pp. 149-156
    • Zaji, A.H.1    Bonakdari, H.2
  • 13
    • 84920254940 scopus 로고    scopus 로고
    • Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting
    • Esmaeili M, Osanloo M, Rashidinejad F, Bazzazi AA, Taji M (2014) Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Eng Comput 30(4):549–558. doi:10.1007/s00366-012-0298-2
    • (2014) Eng Comput , vol.30 , Issue.4 , pp. 549-558
    • Esmaeili, M.1    Osanloo, M.2    Rashidinejad, F.3    Bazzazi, A.A.4    Taji, M.5
  • 14
    • 84910642446 scopus 로고    scopus 로고
    • Pareto genetic design of GMDH-type neural network for predict discharge coefficient in rectangular side orifices
    • Ebtehaj I, Bonakdari H, Khoshbin F, Azimi H (2015) Pareto genetic design of GMDH-type neural network for predict discharge coefficient in rectangular side orifices. Flow Meas Instrum 41:67–74. doi:10.1016/j.flowmeasinst.2014.10.016
    • (2015) Flow Meas Instrum , vol.41 , pp. 67-74
    • Ebtehaj, I.1    Bonakdari, H.2    Khoshbin, F.3    Azimi, H.4
  • 15
    • 84952985636 scopus 로고    scopus 로고
    • Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation
    • Faradonbeh RS, Monjezi M, Armaghani DJ (2015) Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation. Eng Comput. doi:10.1007/s00366-015-0404-3
    • (2015) Eng Comput
    • Faradonbeh, R.S.1    Monjezi, M.2    Armaghani, D.J.3
  • 16
    • 84961061738 scopus 로고    scopus 로고
    • Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
    • Armaghani DJ, Mohamad ET, Hajihassani M, Yagiz S, Motaghedi H (2015) Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Eng Comput. doi:10.1007/s00366-015-0410-5
    • (2015) Eng Comput
    • Armaghani, D.J.1    Mohamad, E.T.2    Hajihassani, M.3    Yagiz, S.4    Motaghedi, H.5
  • 17
    • 84952985963 scopus 로고    scopus 로고
    • A combination of the ICA-ANN model to predict air-overpressure resulting from blasting
    • Armaghani DJ, Hasanipanah M, Mohamad ET (2015) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput. doi:10.1007/s00366-015-0408-z
    • (2015) Eng Comput
    • Armaghani, D.J.1    Hasanipanah, M.2    Mohamad, E.T.3
  • 18
    • 84957621812 scopus 로고    scopus 로고
    • Application of Gene-Expression programming in hydraulic engineering. In: Handbook of Genetic Programming Applications (pp 71–97). Springer International Publishing
    • Zahiri A, Dehghani AA, Azamathulla HMd (2015) Application of Gene-Expression programming in hydraulic engineering. In: Handbook of Genetic Programming Applications (pp 71–97). Springer International Publishing. doi:0.1007/978-3-319-20883-1_4
    • (2015) doi:0.1007/978-3-319-20883-1_4
    • Zahiri, A.1    Dehghani, A.A.2    HMd, A.3
  • 19
    • 34248631948 scopus 로고    scopus 로고
    • Machine Learning Approach to Modeling Sediment Transport
    • Bhattacharya B, Price R, Solomatine D (2007) Machine Learning Approach to Modeling Sediment Transport. J Hydraul Eng 133(4):440–450. doi:10.1061/(ASCE)0733-9429(2007)133:4(440)
    • (2007) J Hydraul Eng , vol.133 , Issue.4 , pp. 440-450
    • Bhattacharya, B.1    Price, R.2    Solomatine, D.3
  • 20
    • 39849091610 scopus 로고    scopus 로고
    • A genetic programming approach to suspended sediment modeling
    • Aytek A, Kisi O (2008) A genetic programming approach to suspended sediment modeling. J Hydrol 351:288–298. doi:10.1016/j.jhydrol.2007.12.005
    • (2008) J Hydrol , vol.351 , pp. 288-298
    • Aytek, A.1    Kisi, O.2
  • 21
    • 82355183599 scopus 로고    scopus 로고
    • Gene-expression programming for sediment transport in sewer pipe systems
    • Ab Ghani A, Azamathulla HMd (2010) Gene-expression programming for sediment transport in sewer pipe systems. J Pipeline Syst Eng Pract 2(3):102–106. doi:10.1061/(ASCE)PS.1949-1204.0000076
    • (2010) J Pipeline Syst Eng Pract , vol.2 , Issue.3 , pp. 102-106
    • Ab Ghani, A.1    Azamathulla, H.M.2
  • 22
    • 84937784552 scopus 로고    scopus 로고
    • Gene expression programming to predict the discharge coefficient in rectangular side weirs
    • Ebtehaj I, Bonakdari H, Zaji AH, Azimi H, Sharifi A (2015) Gene expression programming to predict the discharge coefficient in rectangular side weirs. Appl Soft Comput 35:618–628. doi:10.1016/j.asoc.2015.07.003
    • (2015) Appl Soft Comput , vol.35 , pp. 618-628
    • Ebtehaj, I.1    Bonakdari, H.2    Zaji, A.H.3    Azimi, H.4    Sharifi, A.5
  • 23
    • 84882314134 scopus 로고    scopus 로고
    • Evaluation of sediment transport in sewer using artificial neural network
    • Ebtehaj I, Bonakdari H (2013) Evaluation of sediment transport in sewer using artificial neural network. Eng Appl Comput Fluid Mech 7(3):382–392. doi:10.1080/19942060.2013.11015479
    • (2013) Eng Appl Comput Fluid Mech , vol.7 , Issue.3 , pp. 382-392
    • Ebtehaj, I.1    Bonakdari, H.2
  • 24
    • 84855985774 scopus 로고    scopus 로고
    • ANFIS—based approach for predicting sediment transport in clean sewer
    • Azamathulla HMd, Ab Ghani A, Fei SY (2012) ANFIS—based approach for predicting sediment transport in clean sewer. Appl Soft Comput 12(3):1227–1230. doi:10.1016/j.asoc.2011.12.003
    • (2012) Appl Soft Comput , vol.12 , Issue.3 , pp. 1227-1230
    • Azamathulla, H.M.1    Ab Ghani, A.2    Fei, S.Y.3
  • 25
    • 85027942599 scopus 로고    scopus 로고
    • Performance evaluation of adaptive neural fuzzy inference system for sediment transport in sewers
    • Ebtehaj I, Bonakdari H (2014) Performance evaluation of adaptive neural fuzzy inference system for sediment transport in sewers. Water Resour Manage 28(13):4765–4779. doi:10.1007/s11269-014-0774-0
    • (2014) Water Resour Manage , vol.28 , Issue.13 , pp. 4765-4779
    • Ebtehaj, I.1    Bonakdari, H.2
  • 26
    • 84922354942 scopus 로고    scopus 로고
    • Assessment of evolutionary algorithms in predicting non-deposition sediment transport
    • Ebtehaj I, Bonakdari H (2015) Assessment of evolutionary algorithms in predicting non-deposition sediment transport. Urban Water J. doi:10.1080/1573062X.2014.994003
    • (2015) Urban Water J
    • Ebtehaj, I.1    Bonakdari, H.2
  • 27
    • 84899860143 scopus 로고    scopus 로고
    • Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)
    • Roushangar K, Mehrabani FV, Shiri J (2014) Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs). J Hydrol 514(6):114–122. doi:10.1016/j.jhydrol.2014.03.065
    • (2014) J Hydrol , vol.514 , Issue.6 , pp. 114-122
    • Roushangar, K.1    Mehrabani, F.V.2    Shiri, J.3
  • 28
    • 84893747559 scopus 로고    scopus 로고
    • Incipient sediment transport for non-cohesive landforms by the discrete element method (DEM)
    • Bravo R, Ortiz P, Pérez-Aparicio JL (2014) Incipient sediment transport for non-cohesive landforms by the discrete element method (DEM). Appl Math Model 38(4):1326–1337. doi:10.1016/j.apm.2013.08.010
    • (2014) Appl Math Model , vol.38 , Issue.4 , pp. 1326-1337
    • Bravo, R.1    Ortiz, P.2    Pérez-Aparicio, J.L.3
  • 29
    • 84918827786 scopus 로고    scopus 로고
    • Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe
    • Ebtehaj I, Bonakdari H (2014) Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe. Water Sci Technol 70(10):1695–1701. doi:10.2166/wst.2014.434
    • (2014) Water Sci Technol , vol.70 , Issue.10 , pp. 1695-1701
    • Ebtehaj, I.1    Bonakdari, H.2
  • 30
    • 84941178712 scopus 로고    scopus 로고
    • Assessment of sediment transport approaches for sand-bed rivers by means of machine learning
    • Kitsikoudis V, Sidiropoulos E, Hrissanthou V (2014) Assessment of sediment transport approaches for sand-bed rivers by means of machine learning. Hydrolog Sci J. doi:10.1080/02626667.2014.909599
    • (2014) Hydrolog Sci J
    • Kitsikoudis, V.1    Sidiropoulos, E.2    Hrissanthou, V.3
  • 31
    • 80053987985 scopus 로고    scopus 로고
    • Automatic human knee cartilage segmentation from multi-contrast MR images using extreme learning machines and discriminative random fields
    • Springer, Berlin Heidelberg
    • Zhang K, Lu W (2011) Automatic human knee cartilage segmentation from multi-contrast MR images using extreme learning machines and discriminative random fields. Machine learning in medical imaging. Springer, Berlin Heidelberg, pp 335–343
    • (2011) Machine learning in medical imaging , pp. 335-343
    • Zhang, K.1    Lu, W.2
  • 33
    • 84870253587 scopus 로고    scopus 로고
    • Feature selection for nonlinear models with extreme learning machines
    • Benoit F, Van Heeswijk M, Miche Y, Verleysen M, Lendasse A (2013) Feature selection for nonlinear models with extreme learning machines. Neurocomputing 102(15):111–124. doi:10.1016/j.neucom.2011.12.055
    • (2013) Neurocomputing , vol.102 , Issue.15 , pp. 111-124
    • Benoit, F.1    Van Heeswijk, M.2    Miche, Y.3    Verleysen, M.4    Lendasse, A.5
  • 34
    • 84912569327 scopus 로고    scopus 로고
    • Robust extreme learning machine for regression problems with its application to wifi based indoor positioning system
    • IEEE International Workshop on, IEEE
    • Lu X, Long Y, Zou H, Yu C, Xie L (2014) Robust extreme learning machine for regression problems with its application to wifi based indoor positioning system. In: Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on, IEEE, 1–6
    • (2014) Machine Learning for Signal Processing (MLSP) , vol.2014 , pp. 1-6
    • Lu, X.1    Long, Y.2    Zou, H.3    Yu, C.4    Xie, L.5
  • 35
    • 84914818743 scopus 로고    scopus 로고
    • Spectral–spatial hyperspectral image classification using superpixel and extreme learning machines
    • Springer, Berlin Heidelberg
    • Duan W, Li S, Fang L (2014) Spectral–spatial hyperspectral image classification using superpixel and extreme learning machines. Pattern Recognition. Springer, Berlin Heidelberg, pp 159–167
    • (2014) Pattern Recognition , pp. 159-167
    • Duan, W.1    Li, S.2    Fang, L.3
  • 36
    • 84905387803 scopus 로고    scopus 로고
    • An extreme learning machine approach for slope stability evaluation and prediction
    • Liu Z, Shao J, Xu W, Chen H, Zhang Y (2014) An extreme learning machine approach for slope stability evaluation and prediction. Nat Hazards 73(2):787–804
    • (2014) Nat Hazards , vol.73 , Issue.2 , pp. 787-804
    • Liu, Z.1    Shao, J.2    Xu, W.3    Chen, H.4    Zhang, Y.5
  • 37
    • 84941877275 scopus 로고    scopus 로고
    • Indirect estimation of unconfined compressive strength of carbonate rocks using extreme learning machine
    • Liu Z, Shao J, Xu W, Wu Q (2014) Indirect estimation of unconfined compressive strength of carbonate rocks using extreme learning machine. Acta Geotech. doi:10.1007/s11440-014-0316-1
    • (2014) Acta Geotech
    • Liu, Z.1    Shao, J.2    Xu, W.3    Wu, Q.4
  • 38
    • 0030037787 scopus 로고    scopus 로고
    • Development of design methodology for self-cleansing sewers
    • May RWP, Ackers JC, Butler D (1996) Development of design methodology for self-cleansing sewers. Water Sci Technol 33(9):195–205. doi:10.1016/0273-1223(96)00387-3
    • (1996) Water Sci Technol , vol.33 , Issue.9 , pp. 195-205
    • May, R.W.P.1    Ackers, J.C.2    Butler, D.3
  • 39
    • 0003664970 scopus 로고    scopus 로고
    • Design of sewers to control sediment problems. Report No
    • Construction Industry Research and Information Association, London, UK
    • Ackers JC, Butler D, May RWP (1996) Design of sewers to control sediment problems. Report No. 141 CIRIA, Construction Industry Research and Information Association, London, UK
    • (1996) 141 CIRIA
    • Ackers, J.C.1    Butler, D.2    May, R.W.P.3
  • 40
    • 0003913650 scopus 로고
    • Sediment Transport in Sewers
    • Thesis, University of Newcastle Upon Tyne, UK
    • Ab Ghani A (1993). Sediment Transport in Sewers, Ph.D. Thesis, University of Newcastle Upon Tyne, UK
    • (1993) Ph.D
    • Ab Ghani, A.1
  • 43
    • 33745903481 scopus 로고    scopus 로고
    • Extreme learning machine: theory and applications
    • Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501. doi:10.1016/j.neucom.2005.12.126
    • (2006) Neurocomputing , vol.70 , pp. 489-501
    • Huang, G.B.1    Zhu, Q.Y.2    Siew, C.K.3
  • 44
    • 0038105929 scopus 로고    scopus 로고
    • Radial basis function neural networks for modeling stage discharge relationship
    • Sudheer KP, Jain SK (2003) Radial basis function neural networks for modeling stage discharge relationship. J. Hydrolog Eng 8(3):161–164. doi:10.1061/(ASCE)1084-0699(2003)8:3(161)
    • (2003) J. Hydrolog Eng , vol.8 , Issue.3 , pp. 161-164
    • Sudheer, K.P.1    Jain, S.K.2


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