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Volumn 17, Issue 4, 2012, Pages 528-536

Numerical Model and Computational Intelligence Approaches for Estimating Flow through Rockfill Dam

Author keywords

Artificial neural network; Flow forecast; Genetic algorithm; Numerical method; Rockfill dam

Indexed keywords

DELTA-BAR-DELTA ALGORITHM; EXPERIMENTAL DATA; FLOW FORECAST; FLOWTHROUGH; HYDROGRAPHS; INPUT PARAMETER; NATURAL DISASTERS; ROCK-FILL DAM; WATERSHED MANAGEMENT;

EID: 84860213476     PISSN: 10840699     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)HE.1943-5584.0000446     Document Type: Article
Times cited : (12)

References (54)
  • 1
    • 33744900877 scopus 로고    scopus 로고
    • Artificial neural network based generalized storage-yield-reliability models using the Levenberg-Marquardt algorithm
    • JHYDA7, 0022-1694, 10.1016/j.jhydrol.2005.10.033.
    • Adeloye A.J. De Munari A. Artificial neural network based generalized storage-yield-reliability models using the Levenberg-Marquardt algorithm. J. Hydrol. 2006, 362(1-4):215-230. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2005.10.033.
    • (2006) J. Hydrol. , vol.362 , Issue.1-4 , pp. 215-230
    • Adeloye, A.J.1    De Munari, A.2
  • 2
    • 77949266538 scopus 로고    scopus 로고
    • A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks
    • NRCGEO, 0925-2312, 10.1016/j.neucom.2009.11.007.
    • Almeida L.M. Ludermir T.B. multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks. Neurocomputing 2010, 73(7-9):1438-1450. NRCGEO, 0925-2312, 10.1016/j.neucom.2009.11.007.
    • (2010) Neurocomputing , vol.73 , Issue.7-9 , pp. 1438-1450
    • Almeida, L.M.1    Ludermir, T.B.2
  • 3
    • 33947572974 scopus 로고    scopus 로고
    • A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff
    • JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.01.013.
    • Aqil M. Kita I. Yano A. Nishiyama S. comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. J. Hydrol. 2007, 337(1-2):22-34. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.01.013.
    • (2007) J. Hydrol. , vol.337 , Issue.1-2 , pp. 22-34
    • Aqil, M.1    Kita, I.2    Yano, A.3    Nishiyama, S.4
  • 4
    • 0034174280 scopus 로고    scopus 로고
    • Artificial neural networks in hydrology. I: preliminary concepts
    • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, JHYEFF, 1084-0699, org/10.1061/(ASCE)1084-0699(2000)5:2(115)
    • Artificial neural networks in hydrology. I: preliminary concepts. J. Hydrol. Eng. 2000, 5(2):115-123. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, JHYEFF, 1084-0699, org/10.1061/(ASCE)1084-0699(2000)5:2(115)
    • (2000) J. Hydrol. Eng. , vol.5 , Issue.2 , pp. 115-123
  • 5
    • 33847679664 scopus 로고    scopus 로고
    • Neural network and neuro-fuzzy assessments for scour depth around bridge piers
    • EAAIE6, 0952-1976, 10.1016/j.engappai.2006.06.012.
    • Bateni S.M. Borghei S.M. Jeng D.-S. Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Eng. Appl. Artif. Intell. 2007, 20(3):401-414. EAAIE6, 0952-1976, 10.1016/j.engappai.2006.06.012.
    • (2007) Eng. Appl. Artif. Intell. , vol.20 , Issue.3 , pp. 401-414
    • Bateni, S.M.1    Borghei, S.M.2    Jeng, D.-S.3
  • 6
    • 0034028703 scopus 로고    scopus 로고
    • Flood disasters: lessons from the past-worries for the future
    • Graz, Austria.
    • Berz G. Flood disasters: lessons from the past-worries for the future. Proc. Inst. Civ. Eng. Marit. Eng., 142(1) 2000, 3-8. Graz, Austria.
    • (2000) Proc. Inst. Civ. Eng. Marit. Eng., 142(1) , pp. 3-8
    • Berz, G.1
  • 7
    • 0036221122 scopus 로고    scopus 로고
    • Optimal division of data for neural networks models in water resources applications
    • WRERAQ, 0043-1397, 10.1029/2001WR000266.
    • Bowden G.J. Maier H.R. Dandy G.C. Optimal division of data for neural networks models in water resources applications. Water Resour. Res. 2002, 38(2):1010-1020. WRERAQ, 0043-1397, 10.1029/2001WR000266.
    • (2002) Water Resour. Res. , vol.38 , Issue.2 , pp. 1010-1020
    • Bowden, G.J.1    Maier, H.R.2    Dandy, G.C.3
  • 8
    • 1842426595 scopus 로고    scopus 로고
    • Comparison of static-feedforward and dynamic-feedback neural networks for rainfall-runoff modeling
    • JHYDA7, 0022-1694, 10.1016/j.jhydrol.2003.12.033.
    • Chiang Y.-M. Chang L.-C. Chang F.-J. Comparison of static-feedforward and dynamic-feedback neural networks for rainfall-runoff modeling. J. Hydrol. 2004, 290(3-4):297-311. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2003.12.033.
    • (2004) J. Hydrol. , vol.290 , Issue.3-4 , pp. 297-311
    • Chiang, Y.-M.1    Chang, L.-C.2    Chang, F.-J.3
  • 9
    • 47149115136 scopus 로고    scopus 로고
    • Initial assessment of bridge backwater using an artifical neural network approach
    • CJCEB8, 0315-1468, 10.1139/L07-142.
    • Cobaner M. Seckin G. Kisi O. Initial assessment of bridge backwater using an artifical neural network approach. Can. J. Civ. Eng. 2008, 35(5):500-510. CJCEB8, 0315-1468, 10.1139/L07-142.
    • (2008) Can. J. Civ. Eng. , vol.35 , Issue.5 , pp. 500-510
    • Cobaner, M.1    Seckin, G.2    Kisi, O.3
  • 10
    • 0034621379 scopus 로고    scopus 로고
    • Daily reservoir inflow forecasting using artificial neural networks with stopped training approach
    • JHYDA7, 0022-1694, 10.1016/S0022-1694(00)00214-6.
    • Coulibaly P. Anctil F. Bobée B. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J. Hydrol. 2000, 230(3-4):244-257. JHYDA7, 0022-1694, 10.1016/S0022-1694(00)00214-6.
    • (2000) J. Hydrol. , vol.230 , Issue.3-4 , pp. 244-257
    • Coulibaly, P.1    Anctil, F.2    Bobée, B.3
  • 11
    • 34250818666 scopus 로고    scopus 로고
    • Comparison of neural network methods for infilling missing daily weather records
    • JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.04.020.
    • Coulibaly P. Evora N.D. Comparison of neural network methods for infilling missing daily weather records. J. Hydrol. 2007, 341(1-2):27-41. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.04.020.
    • (2007) J. Hydrol. , vol.341 , Issue.1-2 , pp. 27-41
    • Coulibaly, P.1    Evora, N.D.2
  • 12
    • 0029776836 scopus 로고    scopus 로고
    • An improved genetic algorithm for pipe network optimization
    • WRERAQ, 0043-1397, 10.1029/95WR02917.
    • Dandy G.C. Simpson A.R. Murphy L.J. An improved genetic algorithm for pipe network optimization. Water Resour. Res. 1996, 32(2):449-458. WRERAQ, 0043-1397, 10.1029/95WR02917.
    • (1996) Water Resour. Res. , vol.32 , Issue.2 , pp. 449-458
    • Dandy, G.C.1    Simpson, A.R.2    Murphy, L.J.3
  • 13
    • 0034749335 scopus 로고    scopus 로고
    • Hydrological modeling using artificial neural networks
    • PPGEEC, 0309-1333, org/10.1191/030913301674775671.
    • Dawson C.W. Wilby R.L. Hydrological modeling using artificial neural networks. Prog. Phys. Geog. 2001, 25(1):80-108. PPGEEC, 0309-1333, org/10.1191/030913301674775671.
    • (2001) Prog. Phys. Geog. , vol.25 , Issue.1 , pp. 80-108
    • Dawson, C.W.1    Wilby, R.L.2
  • 14
    • 47249110634 scopus 로고    scopus 로고
    • Prediction of groundwater levels from lake levels and climate data using ANN approach
    • WASADV, 0378-4738.
    • Dogan A. Demirpence H. Cobaner M. Prediction of groundwater levels from lake levels and climate data using ANN approach. Water SA 2008, 34(2):199-208. WASADV, 0378-4738.
    • (2008) Water SA , vol.34 , Issue.2 , pp. 199-208
    • Dogan, A.1    Demirpence, H.2    Cobaner, M.3
  • 15
    • 0242415241 scopus 로고    scopus 로고
    • Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ANN and GP
    • AAINEH, 1087-6545, 10.1080/713827142.
    • Dorado J. Rabuñal J. R. Pazos A. Rivero D. Santos A. Puertas J.R. Prediction and modeling of the rainfall-runoff transformation of typical urban basin using ANN and GP. Appl. Artif. Intell. 2003, 17(4):329-343. AAINEH, 1087-6545, 10.1080/713827142.
    • (2003) Appl. Artif. Intell. , vol.17 , Issue.4 , pp. 329-343
    • Dorado, J.1    Rabuñal, J.R.2    Pazos, A.3    Rivero, D.4    Santos, A.5    Puertas, J.R.6
  • 16
    • 77950516820 scopus 로고    scopus 로고
    • A hybrid neural network and ARIMA model for water quality time series prediction
    • AAINEH, 1087-6545
    • Faruk D.O. hybrid neural network and ARIMA model for water quality time series prediction. Eng. Appl. Artif. Intell. 2009, 23(4):586-594. AAINEH, 1087-6545
    • (2009) Eng. Appl. Artif. Intell. , vol.23 , Issue.4 , pp. 586-594
    • Faruk, D.O.1
  • 17
    • 0032123339 scopus 로고    scopus 로고
    • Runoff forecasting using RBF networks with OLS algorithm
    • JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(1998)3:3(203).
    • Fernando D.A. K. Jayawardena A.W. Runoff forecasting using RBF networks with OLS algorithm. J. Hydrol. Eng. 1998, 3(3):203-209. JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(1998)3:3(203).
    • (1998) J. Hydrol. Eng. , vol.3 , Issue.3 , pp. 203-209
    • Fernando, D.A.K.1    Jayawardena, A.W.2
  • 18
    • 0029223565 scopus 로고
    • Back-propagation neural networks for modelling complex systems
    • AIENEJ, 0954-1810, 10.1016/0954-1810(94)00011-S
    • Goh A.T. C. Back-propagation neural networks for modelling complex systems. Artif. Intell. Eng. 1995, 9(3):143-151. AIENEJ, 0954-1810, 10.1016/0954-1810(94)00011-S
    • (1995) Artif. Intell. Eng. , vol.9 , Issue.3 , pp. 143-151
    • Goh, A.T.C.1
  • 19
    • 0026152931 scopus 로고
    • Hydraulics of flow through a rockfill dam using sediment-free water
    • TAAEAJ, 0001-2351.
    • Herrera N.M. Felton G.K. Hydraulics of flow through rockfill dam using sediment-free water. Trans. 1991, 34(3):871-875. TAAEAJ, 0001-2351.
    • (1991) Trans. , vol.34 , Issue.3 , pp. 871-875
    • Herrera, N.M.1    Felton, G.K.2
  • 20
    • 84860150534 scopus 로고    scopus 로고
    • Two dimensional model of flow through and over rockfill dams and its application in flood control
    • Ph.D. thesis, Faculty of Agriculture, Tarbiat Modares Univ., Tehran, Iran.
    • Heydari M. Two dimensional model of flow through and over rockfill dams and its application in flood control. 2007, 193. Ph.D. thesis, Faculty of Agriculture, Tarbiat Modares Univ., Tehran, Iran.
    • (2007) , pp. 193
    • Heydari, M.1
  • 22
    • 20344367734 scopus 로고    scopus 로고
    • On quantifying agricultural and water management practices from low spatial resolution RS data using genetic algorithms: A numerical study for mixed-pixel environment
    • AWREDI, 0309-1708, 10.1016/j.advwatres.2004.11.015.
    • Ines A.V. M. Honda K. On quantifying agricultural and water management practices from low spatial resolution RS data using genetic algorithms: numerical study for mixed-pixel environment. Adv. Water Resour. 2005, 28(8):856-870. AWREDI, 0309-1708, 10.1016/j.advwatres.2004.11.015.
    • (2005) Adv. Water Resour. , vol.28 , Issue.8 , pp. 856-870
    • Ines, A.V.M.1    Honda, K.2
  • 23
    • 33644655239 scopus 로고    scopus 로고
    • An evaluation of artificial neural network technique for the determination of infiltration model parameters
    • 1568-4946, 10.1016/j.asoc.2004.12.007.
    • Jain A. Kumar A. An evaluation of artificial neural network technique for the determination of infiltration model parameters. Appl. Soft Comput. 2006, 6(3):272-282. 1568-4946, 10.1016/j.asoc.2004.12.007.
    • (2006) Appl. Soft Comput. , vol.6 , Issue.3 , pp. 272-282
    • Jain, A.1    Kumar, A.2
  • 24
    • 2442639370 scopus 로고    scopus 로고
    • Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques
    • 1568-4946, WRERAQ, 0043-1397.
    • Jain A. Srinivasulu S. Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques. Water Resour. Res. 2004, 40(4):W04302. 1568-4946, WRERAQ, 0043-1397.
    • (2004) Water Resour. Res. , vol.40 , Issue.4
    • Jain, A.1    Srinivasulu, S.2
  • 25
    • 0034161355 scopus 로고    scopus 로고
    • Accuracy of neural network approximators in simulation-optimization
    • JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2000)126:2(48).
    • Johnson V.M. Rogers L. Accuracy of neural network approximators in simulation-optimization. J. Water Resour. Plann. Manage. 2000, 126(2):48-56. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2000)126:2(48).
    • (2000) J. Water Resour. Plann. Manage. , vol.126 , Issue.2 , pp. 48-56
    • Johnson, V.M.1    Rogers, L.2
  • 26
    • 42049093796 scopus 로고    scopus 로고
    • Uncertainty reduction of the flood stage forecasting using neural networks model
    • JWRAF5, 1093-474X, 10.1111/j.1752-1688.2007.00144.x
    • Kim S. Kim H.S. Uncertainty reduction of the flood stage forecasting using neural networks model. J. Am. Water Resour. Assoc. 2008, 44(1):148-165. JWRAF5, 1093-474X, 10.1111/j.1752-1688.2007.00144.x
    • (2008) J. Am. Water Resour. Assoc. , vol.44 , Issue.1 , pp. 148-165
    • Kim, S.1    Kim, H.S.2
  • 27
    • 39849084753 scopus 로고    scopus 로고
    • Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling
    • JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.12.014
    • Kim S. Kim H.S. Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J. Hydrol. 2008, 351(3-4):299-317. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.12.014
    • (2008) J. Hydrol. , vol.351 , Issue.3-4 , pp. 299-317
    • Kim, S.1    Kim, H.S.2
  • 28
    • 42949174752 scopus 로고    scopus 로고
    • Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model
    • CGEOEU, 0266-352X, 10.1016/j.compgeo.2007.09.006
    • Kim Y.-S. Kim B.-T. Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model. Comput. Geotech. 2008, 35(3):313-322. CGEOEU, 0266-352X, 10.1016/j.compgeo.2007.09.006
    • (2008) Comput. Geotech. , vol.35 , Issue.3 , pp. 313-322
    • Kim, Y.-S.1    Kim, B.-T.2
  • 29
    • 56649115883 scopus 로고    scopus 로고
    • Parameters affecting the fundamental period of RC buildings with infill walls
    • ENSTDF, 0141-0296, 10.1016/j.engstruct.2008.07.017
    • Kose M.M. Parameters affecting the fundamental period of RC buildings with infill walls. Eng. Struct. 2009, 31(1):93-102. ENSTDF, 0141-0296, 10.1016/j.engstruct.2008.07.017
    • (2009) Eng. Struct. , vol.31 , Issue.1 , pp. 93-102
    • Kose, M.M.1
  • 30
    • 16444365723 scopus 로고    scopus 로고
    • Rainfall-runoff modeling using artificial neural networks: Comparison of network types
    • HYPRE3, 0885-6087, 10.1002/hyp.5581.
    • Kumar A.R. S. Sudheer K.P. Jain S.K. Agarwal P.K. Rainfall-runoff modeling using artificial neural networks: Comparison of network types. Hydrol. Processes 2005, 19(6):1277-1291. HYPRE3, 0885-6087, 10.1002/hyp.5581.
    • (2005) Hydrol. Processes , vol.19 , Issue.6 , pp. 1277-1291
    • Kumar, A.R.S.1    Sudheer, K.P.2    Jain, S.K.3    Agarwal, P.K.4
  • 31
    • 0034746067 scopus 로고    scopus 로고
    • Derivation of Pareto front with genetic algorithm and neural network
    • JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(2001)6:1(52).
    • Liong S.-Y. Khu S.-T. Chan W.-T. Derivation of Pareto front with genetic algorithm and neural network. J. Hydrol. Eng. 2001, 6(1):52-61. JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(2001)6:1(52).
    • (2001) J. Hydrol. Eng. , vol.6 , Issue.1 , pp. 52-61
    • Liong, S.-Y.1    Khu, S.-T.2    Chan, W.-T.3
  • 32
    • 0029663621 scopus 로고    scopus 로고
    • The use of artificial neural networks for prediction of water quality parameters
    • WRERAQ, 0043-1397, 10.1029/96WR03529.
    • Maier H.R. Dandy G.C. The use of artificial neural networks for prediction of water quality parameters. Water Resour. Res. 1996, 32(4):1013-1022. WRERAQ, 0043-1397, 10.1029/96WR03529.
    • (1996) Water Resour. Res. , vol.32 , Issue.4 , pp. 1013-1022
    • Maier, H.R.1    Dandy, G.C.2
  • 33
    • 70350129932 scopus 로고    scopus 로고
    • Bridge afflux analysis through arched bridge constrictions using artificial intelligence methods
    • 1028-6608, 10.1080/10286600802151804.
    • Mamak M. Seckin G. Cobaner M. Kisi O. Bridge afflux analysis through arched bridge constrictions using artificial intelligence methods. Civ. Eng. Environ. Syst. 2009, 26(3):279-293. 1028-6608, 10.1080/10286600802151804.
    • (2009) Civ. Eng. Environ. Syst. , vol.26 , Issue.3 , pp. 279-293
    • Mamak, M.1    Seckin, G.2    Cobaner, M.3    Kisi, O.4
  • 34
    • 0037306187 scopus 로고    scopus 로고
    • Variance decomposition-based sensitivity analysis via neural networks
    • RESSEP, 0951-8320, 10.1016/S0951-8320(02)00234-X.
    • Marseguerra M. Masini R. Zio E. Cojazzi G. Variance decomposition-based sensitivity analysis via neural networks. Reliab. Eng. Syst. Saf. 2003, 79(2):229-238. RESSEP, 0951-8320, 10.1016/S0951-8320(02)00234-X.
    • (2003) Reliab. Eng. Syst. Saf. , vol.79 , Issue.2 , pp. 229-238
    • Marseguerra, M.1    Masini, R.2    Zio, E.3    Cojazzi, G.4
  • 35
    • 67650293317 scopus 로고    scopus 로고
    • Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed
    • HYPRE3, 0885-6087, 10.1002/hyp.7136.
    • Mutlu E. Chaubey I. Hexmoor H. Bajwa S.G. Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrol. Processes 2008, 22(26):5097-5106. HYPRE3, 0885-6087, 10.1002/hyp.7136.
    • (2008) Hydrol. Processes , vol.22 , Issue.26 , pp. 5097-5106
    • Mutlu, E.1    Chaubey, I.2    Hexmoor, H.3    Bajwa, S.G.4
  • 36
    • 34248343038 scopus 로고    scopus 로고
    • A numerical modelling and neural network approach to estimate the impact of groundwater abstractions on river flows
    • JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.01.041.
    • Parkin G. Birkinshaw S.J. Younger P.L. Rao Z. Kirk S. numerical modelling and neural network approach to estimate the impact of groundwater abstractions on river flows. J. Hydrol. 2007, 339(1-2):15-28. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.01.041.
    • (2007) J. Hydrol. , vol.339 , Issue.1-2 , pp. 15-28
    • Parkin, G.1    Birkinshaw, S.J.2    Younger, P.L.3    Rao, Z.4    Kirk, S.5
  • 37
    • 38349082292 scopus 로고    scopus 로고
    • A coupled model tree-genetic algorithm scheme for flow and water quality predictions in watersheds
    • JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.11.013.
    • Preis A. Ostfeld A. coupled model tree-genetic algorithm scheme for flow and water quality predictions in watersheds. J. Hydrol. 2008, 349(3-4):364-375. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.11.013.
    • (2008) J. Hydrol. , vol.349 , Issue.3-4 , pp. 364-375
    • Preis, A.1    Ostfeld, A.2
  • 38
    • 71649087374 scopus 로고    scopus 로고
    • Using artificial neural networks to generate synthetic well logs
    • JNGSA4, 1875-5100, doi.org/10.1016/j.jngse.2009.08.003.
    • Rolon L. Mohaghegh S.D. Ameri S. Gaskari R. McDaniel B. Using artificial neural networks to generate synthetic well logs. J. Nat. Gas Sci. Eng. 2009, 1(4-5):118-133. JNGSA4, 1875-5100, doi.org/10.1016/j.jngse.2009.08.003.
    • (2009) J. Nat. Gas Sci. Eng. , vol.1 , Issue.4-5 , pp. 118-133
    • Rolon, L.1    Mohaghegh, S.D.2    Ameri, S.3    Gaskari, R.4    McDaniel, B.5
  • 40
    • 71349085742 scopus 로고    scopus 로고
    • Modelling of time related drying changes on matte coated paper with artificial neural networks
    • ESAPEH, 0957-4174, 10.1016/j.eswa.2009.09.068.
    • Şahïnbaşkan T. Köse E. Modelling of time related drying changes on matte coated paper with artificial neural networks. Expert Syst. Appl. 2010, 37(4):3140-3144. ESAPEH, 0957-4174, 10.1016/j.eswa.2009.09.068.
    • (2010) Expert Syst. Appl. , vol.37 , Issue.4 , pp. 3140-3144
    • Şahïnbaşkan, T.1    Köse, E.2
  • 41
    • 46149092775 scopus 로고    scopus 로고
    • Micro genetic algorithms and artificial neural networks to assess minimum data requirements for prediction of pesticide concentrations in shallow groundwater on a regional scale
    • WRERAQ, 0043-1397.
    • Sahoo G.B. Ray C. Micro genetic algorithms and artificial neural networks to assess minimum data requirements for prediction of pesticide concentrations in shallow groundwater on regional scale. Water Resour. Res. 2008, 44:W05414. WRERAQ, 0043-1397.
    • (2008) Water Resour. Res. , vol.44
    • Sahoo, G.B.1    Ray, C.2
  • 42
    • 33745982644 scopus 로고    scopus 로고
    • Application of artificial neural networks to assess pesticide contamination in shallow groundwater
    • STENDL, 0048-9697, 10.1016/j.scitotenv.2005.12.011.
    • Sahoo G.B. Ray C. Mehnert E. Keefer D.A. Application of artificial neural networks to assess pesticide contamination in shallow groundwater. Sci. Total Environ. 2006, 367(1):234-251. STENDL, 0048-9697, 10.1016/j.scitotenv.2005.12.011.
    • (2006) Sci. Total Environ. , vol.367 , Issue.1 , pp. 234-251
    • Sahoo, G.B.1    Ray, C.2    Mehnert, E.3    Keefer, D.A.4
  • 43
    • 84860175297 scopus 로고    scopus 로고
    • Reservoir Routing through successive rockfill detention dams
    • Samani J.M. V. Heydari M. Reservoir Routing through successive rockfill detention dams. J. Agric. Sci. Technol. 2007, 9(4):317-326
    • (2007) J. Agric. Sci. Technol. , vol.9 , Issue.4 , pp. 317-326
    • Samani, J.M.V.1    Heydari, M.2
  • 44
    • 60649118396 scopus 로고    scopus 로고
    • Artificial neural network modeling of the river water quality-A case study
    • ECMODT, 0304-3800, 10.1016/j.ecolmodel.2009.01.004.
    • Singh K.P. Basant A. Malik A. Jain G. Artificial neural network modeling of the river water quality-A case study. Ecol. Modell. 2009, 220(6):888-895. ECMODT, 0304-3800, 10.1016/j.ecolmodel.2009.01.004.
    • (2009) Ecol. Modell. , vol.220 , Issue.6 , pp. 888-895
    • Singh, K.P.1    Basant, A.2    Malik, A.3    Jain, G.4
  • 45
    • 60449106698 scopus 로고    scopus 로고
    • Development of artificial neural network model for a coal-fired boiler using real plant data
    • ENGYD4, 0149-9386, 10.1016/j.energy.2008.10.010.
    • Smrekar J. Assadi M. Fast M. Kuštrin I. De S. Development of artificial neural network model for coal-fired boiler using real plant data. Energy 2009, 34(2):144-152. ENGYD4, 0149-9386, 10.1016/j.energy.2008.10.010.
    • (2009) Energy , vol.34 , Issue.2 , pp. 144-152
    • Smrekar, J.1    Assadi, M.2    Fast, M.3    Kuštrin, I.4    De, S.5
  • 46
    • 33644636765 scopus 로고    scopus 로고
    • A comparative analysis of training methods for artificial neural network rainfall-runoff models
    • 1568-4946, 10.1016/j.asoc.2005.02.002.
    • Srinivasulu S. Jain A. comparative analysis of training methods for artificial neural network rainfall-runoff models. Appl. Soft Comput. 2006, 6(3):295-306. 1568-4946, 10.1016/j.asoc.2005.02.002.
    • (2006) Appl. Soft Comput. , vol.6 , Issue.3 , pp. 295-306
    • Srinivasulu, S.1    Jain, A.2
  • 47
    • 77952883472 scopus 로고    scopus 로고
    • Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran
    • 10.1007/s00521-009-0320-9
    • Tabari H. Marofi S. Abyaneh H. Z. Sharifi M.R. Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran. Neural Comput. Appl. 2010, 19(4):625-635. 10.1007/s00521-009-0320-9
    • (2010) Neural Comput. Appl. , vol.19 , Issue.4 , pp. 625-635
    • Tabari, H.1    Marofi, S.2    Abyaneh, H.Z.3    Sharifi, M.R.4
  • 48
    • 77953692036 scopus 로고    scopus 로고
    • Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression
    • IRSCD2, 0342-7188, org/10.1007/s00271-009-0201-0
    • Tabari H. Marofi S. Sabziparvar A.A. Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrig. Sci. 2010, 28(5):399-406. IRSCD2, 0342-7188, org/10.1007/s00271-009-0201-0
    • (2010) Irrig. Sci. , vol.28 , Issue.5 , pp. 399-406
    • Tabari, H.1    Marofi, S.2    Sabziparvar, A.A.3
  • 49
    • 78650738248 scopus 로고    scopus 로고
    • Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region
    • MAPHEU, 1436-5065, 10.1007/s00703-010-0110-z.
    • Tabari H. Sabziparvar A.A. Ahmadi M. Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteorol. Atmos. Phys. 2010, 110(3-4):135-142. MAPHEU, 1436-5065, 10.1007/s00703-010-0110-z.
    • (2010) Meteorol. Atmos. Phys. , vol.110 , Issue.3-4 , pp. 135-142
    • Tabari, H.1    Sabziparvar, A.A.2    Ahmadi, M.3
  • 50
    • 0033167344 scopus 로고    scopus 로고
    • Rainfall-runoff modeling using artificial neural networks
    • JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(1999)4:3(232).
    • Tokar A.S. Johnson P.A. Rainfall-runoff modeling using artificial neural networks. J. Hydrol. Eng. 1999, 4(3):232-239. JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(1999)4:3(232).
    • (1999) J. Hydrol. Eng. , vol.4 , Issue.3 , pp. 232-239
    • Tokar, A.S.1    Johnson, P.A.2
  • 51
    • 20444494282 scopus 로고    scopus 로고
    • Case study: Finite element method and artificial neural network models for flow through Jeziorsko earthfill dam in Poland
    • JHYEFF, 1084-0699, 10.1061/(ASCE)0733-9429(2005)131:6(431).
    • Tayfur G. Swiatek D. Wita A. Singh V.P. Case study: Finite element method and artificial neural network models for flow through Jeziorsko earthfill dam in Poland. J. Hydrol. Eng. 2005, 131(6):431-440. JHYEFF, 1084-0699, 10.1061/(ASCE)0733-9429(2005)131:6(431).
    • (2005) J. Hydrol. Eng. , vol.131 , Issue.6 , pp. 431-440
    • Tayfur, G.1    Swiatek, D.2    Wita, A.3    Singh, V.P.4
  • 52
    • 70449527356 scopus 로고    scopus 로고
    • Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels
    • AESODT, 0965-9978, 10.1016/j.advengsoft.2009.10.002.
    • Unal B. Mamak M. Seckin G. Cobaner M. Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels. Adv. Eng. Software 2010, 41(2):120-129. AESODT, 0965-9978, 10.1016/j.advengsoft.2009.10.002.
    • (2010) Adv. Eng. Software , vol.41 , Issue.2 , pp. 120-129
    • Unal, B.1    Mamak, M.2    Seckin, G.3    Cobaner, M.4
  • 53
    • 28844433086 scopus 로고    scopus 로고
    • Modelling combined open channel flow by artificial neural networks
    • HYPRE3, 0885-6087, 10.1002/hyp.5858.
    • Yang H.C. Chang F.J. Modelling combined open channel flow by artificial neural networks. Hydrol. Processes 2005, 19(18):3747-3762. HYPRE3, 0885-6087, 10.1002/hyp.5858.
    • (2005) Hydrol. Processes , vol.19 , Issue.18 , pp. 3747-3762
    • Yang, H.C.1    Chang, F.J.2
  • 54
    • 36349001715 scopus 로고    scopus 로고
    • An intelligent displacement back-analysis method for earth-rockfill dams
    • CGEOEU, 0266-352X, 10.1016/j.compgeo.2007.03.002.
    • Yu Y. Zhang B. Yuan H. An intelligent displacement back-analysis method for earth-rockfill dams. Comput. Geotech. 2007, 34(6):423-434. CGEOEU, 0266-352X, 10.1016/j.compgeo.2007.03.002.
    • (2007) Comput. Geotech. , vol.34 , Issue.6 , pp. 423-434
    • Yu, Y.1    Zhang, B.2    Yuan, H.3


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