메뉴 건너뛰기




Volumn 317, Issue 3-4, 2006, Pages 291-306

Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques

Author keywords

Artificial neural networks; Black box and gray box models; Conceptual models; Hydrologic modelling; Rainfall runoff modelling; Self organizing networks

Indexed keywords

EVAPOTRANSPIRATION; MOISTURE; NEURAL NETWORKS; RAIN; SOILS;

EID: 28844473522     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2005.05.022     Document Type: Article
Times cited : (121)

References (35)
  • 1
    • 0034254196 scopus 로고    scopus 로고
    • Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments
    • R.J. Abrahart, and L. See Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments Hydrol. Process. 14 2000 2157 2172
    • (2000) Hydrol. Process. , vol.14 , pp. 2157-2172
    • Abrahart, R.J.1    See, L.2
  • 2
    • 11144345954 scopus 로고    scopus 로고
    • An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition
    • F. Anctil, and D.G. Tape An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition J. Env. Eng. Sci. 3 1 2004 S121 S128
    • (2004) J. Env. Eng. Sci. , vol.3 , Issue.1
    • Anctil, F.1    Tape, D.G.2
  • 3
    • 0000782355 scopus 로고
    • Automated base flow separation and recession analysis techniques
    • J.G. Arnold, P.M. Allen, R. Muttiah, and G. Bernhardt Automated base flow separation and recession analysis techniques Ground Water 33 6 1995 1010 1018
    • (1995) Ground Water , vol.33 , Issue.6 , pp. 1010-1018
    • Arnold, J.G.1    Allen, P.M.2    Muttiah, R.3    Bernhardt, G.4
  • 6
    • 0032688155 scopus 로고    scopus 로고
    • River flood forecasting with neural network model
    • M. Campolo, P. Andreussi, and A. Soldati River flood forecasting with neural network model Water Resour. Res. 35 4 1999 1191 1197
    • (1999) Water Resour. Res. , vol.35 , Issue.4 , pp. 1191-1197
    • Campolo, M.1    Andreussi, P.2    Soldati, A.3
  • 7
    • 0026954346 scopus 로고
    • Forecasting the behaviour of the multivariate time series using neural networks
    • K. Chakraborty, K. Mehrotra, C.K. Mohan, and S. Ranka Forecasting the behaviour of the multivariate time series using neural networks Neural Networks 5 1992 961 970
    • (1992) Neural Networks , vol.5 , pp. 961-970
    • Chakraborty, K.1    Mehrotra, K.2    Mohan, C.K.3    Ranka, S.4
  • 8
    • 0032005702 scopus 로고    scopus 로고
    • An artificial neural network approach to rainfall-runoff modeling
    • D.W. Dawson, and R. Wilby An artificial neural network approach to rainfall-runoff modeling Hydrol. Sci. J. 43 1 1998 47 65
    • (1998) Hydrol. Sci. J. , vol.43 , Issue.1 , pp. 47-65
    • Dawson, D.W.1    Wilby, R.2
  • 9
    • 0031998129 scopus 로고    scopus 로고
    • Application example of neural networks for time series analysis: Rainfall runoff modeling
    • D. Furundzic Application example of neural networks for time series analysis: rainfall runoff modeling Signal Process. 64 1998 383 396
    • (1998) Signal Process. , vol.64 , pp. 383-396
    • Furundzic, D.1
  • 10
    • 0004534895 scopus 로고
    • A water yield model for small watersheds
    • C.T. Haan A water yield model for small watersheds Water Resour. Res. 8 1 1972 58 69
    • (1972) Water Resour. Res. , vol.8 , Issue.1 , pp. 58-69
    • Haan, C.T.1
  • 11
    • 0029413797 scopus 로고
    • Artificial neural network modeling of the rainfall-runoff process
    • K.L. Hsu, H.V. Gupta, and S. Sorooshian Artificial neural network modeling of the rainfall-runoff process Wat. Resour. Res. 31 10 1995 2517 2530
    • (1995) Wat. Resour. Res. , vol.31 , Issue.10 , pp. 2517-2530
    • Hsu, K.L.1    Gupta, H.V.2    Sorooshian, S.3
  • 12
    • 0036998831 scopus 로고    scopus 로고
    • Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis
    • K.L. Hsu, H.V. Gupta, X. Gao, S. Sorooshian, and B. Imam Self-organizing linear output map (SOLO): an artificial neural network suitable for hydrologic modeling and analysis Water Resour. Res. 38 12 2002 1302 (doi:10.1029/ 2001WR000795)
    • (2002) Water Resour. Res. , vol.38 , Issue.12 , pp. 1302
    • Hsu, K.L.1    Gupta, H.V.2    Gao, X.3    Sorooshian, S.4    Imam, B.5
  • 13
    • 0036640826 scopus 로고    scopus 로고
    • Evaluation of short-term water demand forecast modeling techniques: Conventional methods versus AI
    • A. Jain, and L.E. Ormsbee Evaluation of short-term water demand forecast modeling techniques: conventional methods versus AI J. Am. Water Works Assoc. 94 7 2002 64 72
    • (2002) J. Am. Water Works Assoc. , vol.94 , Issue.7 , pp. 64-72
    • Jain, A.1    Ormsbee, L.E.2
  • 14
    • 0037340658 scopus 로고    scopus 로고
    • Comparative analysis of event based rainfall-runoff modeling techniques-deterministic, statistical, and artificial neural networks'
    • A. Jain, and S.K.V.P. Indurthy Comparative analysis of event based rainfall-runoff modeling techniques-deterministic, statistical, and artificial neural networks' J. Hydrol. Engg., ASCE 8 2 2003 1 6
    • (2003) J. Hydrol. Engg., ASCE , vol.8 , Issue.2 , pp. 1-6
    • Jain, A.1    Indurthy, S.K.V.P.2
  • 15
    • 12244280869 scopus 로고    scopus 로고
    • An evaluation of the available techniques for estimating missing fecal coliform data
    • A. Jain, and L.E. Ormsbee An evaluation of the available techniques for estimating missing fecal coliform data J. Am. Water Resour. Assoc. 40 6 2004 1617 1630
    • (2004) J. Am. Water Resour. Assoc. , vol.40 , Issue.6 , pp. 1617-1630
    • Jain, A.1    Ormsbee, L.E.2
  • 16
    • 2442639370 scopus 로고    scopus 로고
    • Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded GA, and ANN techniques
    • A. Jain, and S. Srinivasulu Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded GA, and ANN techniques Water Resour. Res. 40 4 2004 (W04302, doi:10.1029/2003WR002355)
    • (2004) Water Resour. Res. , vol.40 , Issue.4
    • Jain, A.1    Srinivasulu, S.2
  • 17
    • 0035494446 scopus 로고    scopus 로고
    • Short-term water demand forecast modeling at IIT Kanpur using artificial neural networks
    • A. Jain, A.K. Varshney, and U.C. Joshi Short-term water demand forecast modeling at IIT Kanpur using artificial neural networks Water Resour. Manage. 15 5 2001 299 321
    • (2001) Water Resour. Manage. , vol.15 , Issue.5 , pp. 299-321
    • Jain, A.1    Varshney, A.K.2    Joshi, U.C.3
  • 18
    • 1542287371 scopus 로고    scopus 로고
    • Identification of physical processes inherent in artificial neural network rainfall runoff models?
    • A. Jain, K.P. Sudheer, and S. Srinivasulu Identification of physical processes inherent in artificial neural network rainfall runoff models? Hydrol. Process. 118 3 2004 571 581
    • (2004) Hydrol. Process. , vol.118 , Issue.3 , pp. 571-581
    • Jain, A.1    Sudheer, K.P.2    Srinivasulu, S.3
  • 19
    • 85001858096 scopus 로고    scopus 로고
    • Discussion on rainfall runoff modeling using artificial neural networks
    • A. Kumar, and K. Minocha Discussion on rainfall runoff modeling using artificial neural networks J. Hydrol. Eng., ASCE 6 2 2001 176 177
    • (2001) J. Hydrol. Eng., ASCE , vol.6 , Issue.2 , pp. 176-177
    • Kumar, A.1    Minocha, K.2
  • 20
    • 0034610444 scopus 로고    scopus 로고
    • Rainfall-runoff relations for Karstic springs. Part II: Continuous wavelet and discrete orthogonal multiresolution
    • D. Labat, R. Ababou, and A. Mangin Rainfall-runoff relations for Karstic springs. Part II: continuous wavelet and discrete orthogonal multiresolution J. Hydrol. 238 3-4 2000 149 178
    • (2000) J. Hydrol. , vol.238 , Issue.3-4 , pp. 149-178
    • Labat, D.1    Ababou, R.2    Mangin, A.3
  • 21
    • 0034931357 scopus 로고    scopus 로고
    • Introduction of wavelet analyses to rainfall-runoff relationship for a Karstic basin: The case of Licq-Atherey Karstic system, France
    • D. Labat, R. Ababou, and A. Mangin Introduction of wavelet analyses to rainfall-runoff relationship for a Karstic basin: The case of Licq-Atherey Karstic system, France Ground Water 39 4 2001 605 615
    • (2001) Ground Water , vol.39 , Issue.4 , pp. 605-615
    • Labat, D.1    Ababou, R.2    Mangin, A.3
  • 22
    • 0038456946 scopus 로고    scopus 로고
    • Application of wavelet transform in runoff sequence analysis
    • S.Y. Liu, X.Z. Quan, and Y.C. Zhang Application of wavelet transform in runoff sequence analysis Progr. Nat. Sci. 13 7 2003 546 549
    • (2003) Progr. Nat. Sci. , vol.13 , Issue.7 , pp. 546-549
    • Liu, S.Y.1    Quan, X.Z.2    Zhang, Y.C.3
  • 23
    • 0030159380 scopus 로고    scopus 로고
    • Artificial neural networks as rainfall runoff models
    • A.W. Minns, and M.J. Hall Artificial neural networks as rainfall runoff models Hydrol. Sci. J. 41 3 1996 399 417
    • (1996) Hydrol. Sci. J. , vol.41 , Issue.3 , pp. 399-417
    • Minns, A.W.1    Hall, M.J.2
  • 24
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • D.E. Rumelhart, G.E. Hinton, and R.J. Williams Learning representations by back-propagating errors Nature 323 1986 533 536
    • (1986) Nature , vol.323 , pp. 533-536
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 25
    • 0033535432 scopus 로고    scopus 로고
    • A non-linear rainfall-runoff model using an artificial neural network
    • N. Sajikumar, and B.S. Thandaveswara A non-linear rainfall-runoff model using an artificial neural network J. Hydrol. 216 1999 32 55
    • (1999) J. Hydrol. , vol.216 , pp. 32-55
    • Sajikumar, N.1    Thandaveswara, B.S.2
  • 26
    • 0342506462 scopus 로고    scopus 로고
    • Application of a neural network technique to rainfall-runoff modeling
    • A.Y. Shamseldin Application of a neural network technique to rainfall-runoff modeling J. Hydrol. 199 1997 272 294
    • (1997) J. Hydrol. , vol.199 , pp. 272-294
    • Shamseldin, A.Y.1
  • 27
    • 0029416249 scopus 로고
    • Neural network models of the rainfall runoff process
    • J. Smith, and R.N. Eli Neural network models of the rainfall runoff process ASCE J. Res. Plan. Manage. 121 1995 499 508
    • (1995) ASCE J. Res. Plan. Manage. , vol.121 , pp. 499-508
    • Smith, J.1    Eli, R.N.2
  • 28
    • 0032003006 scopus 로고    scopus 로고
    • Streamflow characterization and feature detection using a discrete wavelet transform
    • L.C. Smith, D.L. Turcotte, and B.L. Isacks Streamflow characterization and feature detection using a discrete wavelet transform Hydrol. Process. 12 2 1998 233 249
    • (1998) Hydrol. Process. , vol.12 , Issue.2 , pp. 233-249
    • Smith, L.C.1    Turcotte, D.L.2    Isacks, B.L.3
  • 29
    • 0034060297 scopus 로고    scopus 로고
    • Spectral analysis of base flow separation with digital filters
    • M.E. Spongberg Spectral analysis of base flow separation with digital filters Water Resour. Res. 36 3 2000 745 752
    • (2000) Water Resour. Res. , vol.36 , Issue.3 , pp. 745-752
    • Spongberg, M.E.1
  • 30
    • 1642333234 scopus 로고    scopus 로고
    • Explaining the internal behavior of artificial neural network river flow models
    • K.P. Sudheer, and A. Jain Explaining the internal behavior of artificial neural network river flow models Hydrol. Process. 118 4 2004 833 844
    • (2004) Hydrol. Process. , vol.118 , Issue.4 , pp. 833-844
    • Sudheer, K.P.1    Jain, A.2
  • 31
    • 0034174397 scopus 로고    scopus 로고
    • Precipitation runoff modeling using artificial neural network and conceptual models
    • A.S. Tokar, and M. Markus Precipitation runoff modeling using artificial neural network and conceptual models J. Hydrol. Eng., ASCE 5 2 2000 156 161
    • (2000) J. Hydrol. Eng., ASCE , vol.5 , Issue.2 , pp. 156-161
    • Tokar, A.S.1    Markus, M.2
  • 32
    • 0037388711 scopus 로고    scopus 로고
    • Detection of conceptual model rainfall-runoff processes inside an artificial neural network
    • R.L. Wilby, R.J. Abrahart, and C.W. Dawson Detection of conceptual model rainfall-runoff processes inside an artificial neural network Hydrol. Sci. J. 48 2 2003 163 181
    • (2003) Hydrol. Sci. J. , vol.48 , Issue.2 , pp. 163-181
    • Wilby, R.L.1    Abrahart, R.J.2    Dawson, C.W.3
  • 33
    • 0034100712 scopus 로고    scopus 로고
    • Prediction of Watershed Runoff using Bayesian Concepts and Modular Neural Networks
    • B. Zhang, and S. Govindaraju Prediction of Watershed Runoff using Bayesian Concepts and Modular Neural Networks Water Resour. Res. 36 3 2000 753 762
    • (2000) Water Resour. Res. , vol.36 , Issue.3 , pp. 753-762
    • Zhang, B.1    Govindaraju, S.2
  • 35
    • 0003433293 scopus 로고    scopus 로고
    • Jaico Publishing House, Mumbai, India, in arrangement with West Publishing Company St Paul, Minnesota
    • M.J. Zurada An Introduction to Artificial Neural Systems 1997 Jaico Publishing House, Mumbai, India, in arrangement with West Publishing Company St Paul, Minnesota
    • (1997) An Introduction to Artificial Neural Systems
    • Zurada, M.J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.