메뉴 건너뛰기




Volumn 10, Issue 4, 2006, Pages 603-608

Optimising training data for ANNs with genetic algorithms

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DATA SET; FLOW MODELING; GENETIC ALGORITHM; HYDROLOGY; NUMERICAL MODEL; RAINFALL-RUNOFF MODELING;

EID: 33748550262     PISSN: 10275606     EISSN: 16077938     Source Type: Journal    
DOI: 10.5194/hess-10-603-2006     Document Type: Article
Times cited : (16)

References (21)
  • 1
    • 14344261493 scopus 로고    scopus 로고
    • Generalisation for neural networks through data sampling and training procedures with applications to streamflow predictions
    • Anctil, F. and Lauzon, N.: Generalisation for neural networks through data sampling and training procedures with applications to streamflow predictions, Hydrol. Earth Syst. Sci., 8, 940-958, 2004, http://www.hydrol- earth-syst-sci.net/8/940/2004/.
    • (2004) Hydrol. Earth Syst. Sci. , vol.8 , pp. 940-958
    • Anctil, F.1    Lauzon, N.2
  • 2
    • 0002158924 scopus 로고    scopus 로고
    • Genetic programming as a model induction engine
    • Babovic, V. and Keijzer, M.: Genetic programming as a model induction engine, J. Hydroinformatics, 2(1), 35-60, 2000.
    • (2000) J. Hydroinformatics , vol.2 , Issue.1 , pp. 35-60
    • Babovic, V.1    Keijzer, M.2
  • 3
    • 0030646548 scopus 로고    scopus 로고
    • The evolution of equations from hydraulic data: Part I - Theory
    • Babovic, V. and Abbott, M. B.: The evolution of equations from hydraulic data: Part I - Theory, J. Hydraulic Res., 35, 3, 1-14, 1997.
    • (1997) J. Hydraulic Res. , vol.35 , Issue.3 , pp. 1-14
    • Babovic, V.1    Abbott, M.B.2
  • 4
    • 0036221122 scopus 로고    scopus 로고
    • Optimal division of data for neural network models in water resources applications
    • Bowden, G. J., Holger, R. M., and Dandy, G. C.: Optimal division of data for neural network models in water resources applications, Water Resour. Res., 38(2), 1-11, 2002.
    • (2002) Water Resour. Res. , vol.38 , Issue.2 , pp. 1-11
    • Bowden, G.J.1    Holger, R.M.2    Dandy, G.C.3
  • 5
    • 34250192033 scopus 로고    scopus 로고
    • Model induction from data: Towards the next generation of computational engines in hydraulics and hydrology
    • Delft
    • Dibike, Y. B.: Model Induction from Data: Towards the next generation of computational engines in hydraulics and hydrology, IHE Delft, Delft, 2002.
    • (2002) IHE Delft
    • Dibike, Y.B.1
  • 6
    • 33748542651 scopus 로고    scopus 로고
    • Derivation of effective and efficient dataset with subtractive clustering method and genetic algorithm
    • Doan, C. D., Liong, S. Y., and Karunasinghe, D. S. K.: Derivation of effective and efficient dataset with subtractive clustering method and genetic algorithm, J. Hydroinformatics, 7, 219-233, 2005.
    • (2005) J. Hydroinformatics , vol.7 , pp. 219-233
    • Doan, C.D.1    Liong, S.Y.2    Karunasinghe, D.S.K.3
  • 7
    • 1642618054 scopus 로고    scopus 로고
    • Application of a distributed physically-based hydrological model to a medium size catchment
    • Feyen, L., Vázquez, R., Christiaens, K., Sels, O., and Feyen, J.: Application of a distributed physically-based hydrological model to a medium size catchment, Hydrol. Earth Syst. Sci., 4, 47-63, 2000, http://www.hydrol- earth-syst-sci.net/4/47/2000/.
    • (2000) Hydrol. Earth Syst. Sci. , vol.4 , pp. 47-63
    • Feyen, L.1    Vázquez, R.2    Christiaens, K.3    Sels, O.4    Feyen, J.5
  • 11
    • 0003308797 scopus 로고
    • A genetic algorithm for function optimization: A MATLAB implementation
    • Houck, C., Joines, J., and Kay, M.: A genetic algorithm for function optimization: a MATLAB implementation, NCSU-IE TR, 1995.
    • (1995) NCSU-IE TR
    • Houck, C.1    Joines, J.2    Kay, M.3
  • 13
    • 0030159380 scopus 로고    scopus 로고
    • Artificial neural networks as rainfallrunoff models
    • Minns, A. W. and Hall, M. J.: Artificial neural networks as rainfallrunoff models, Hydrol. Sci. J., 41(3), 399-417, 1996.
    • (1996) Hydrol. Sci. J. , vol.41 , Issue.3 , pp. 399-417
    • Minns, A.W.1    Hall, M.J.2
  • 14
    • 0013015416 scopus 로고    scopus 로고
    • Subsymbolic methods for data mining in hydraulic engineering
    • Minns, A. W.: Subsymbolic methods for data mining in hydraulic engineering, J. Hydroinformatics, 2, 3-13, 2000.
    • (2000) J. Hydroinformatics , vol.2 , pp. 3-13
    • Minns, A.W.1
  • 17
    • 27644448548 scopus 로고    scopus 로고
    • Simulation of flood flow in a river system using artificial neural networks
    • Shrestha, R. G., Theobald, S., and Nestmann, F.: Simulation of flood flow in a river system using artificial neural networks, Hydrol. Earth Syst. Sci., 9, 313-321,
    • Hydrol. Earth Syst. Sci. , vol.9 , pp. 313-321
    • Shrestha, R.G.1    Theobald, S.2    Nestmann, F.3
  • 18
    • 0037565156 scopus 로고    scopus 로고
    • Model trees as an alternative to neural networks in trainfall-runoff modelling
    • Solomatine, D. P. and Dulal, K. N.: Model trees as an alternative to neural networks in trainfall-runoff modelling, Hydrol. Sci. J., 48, 3, 399-411, 2003.
    • (2003) Hydrol. Sci. J. , vol.48 , Issue.3 , pp. 399-411
    • Solomatine, D.P.1    Dulal, K.N.2
  • 19
    • 23744444467 scopus 로고    scopus 로고
    • Constraints of artificial neural networks for rainfall-runoff modelling: Trade-offs in hydrological state representation and model evaluation
    • de Vos, N. J. and Rientjes, T. H. M.: Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation, Hydrol. Earth Syst. Sci., 9, 111-126, 2005, http://www.hydrol-earth-syst-sci.net/9/111/2005/.
    • (2005) Hydrol. Earth Syst. Sci. , vol.9 , pp. 111-126
    • De Vos, N.J.1    Rientjes, T.H.M.2
  • 20
    • 0035105632 scopus 로고    scopus 로고
    • Modelling rainfall-runoff using genetic programming
    • Whigham, P. A. and Crapper, P. F.: Modelling rainfall-runoff using genetic programming, Mathematical and Computer Modelling, 33(6-7), 707-721, 2001.
    • (2001) Mathematical and Computer Modelling , vol.33 , Issue.6-7 , pp. 707-721
    • Whigham, P.A.1    Crapper, P.F.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.