메뉴 건너뛰기




Volumn 331, Issue , 2009, Pages 51-57

Appropriate data normalization range for daily river flow forecasting using an artificial neural network

Author keywords

Artificial neural network; Data normalization; River flow forecasting

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DATA NORMALIZATION; DATA RANGES; DATA SETS; EAST COAST; FORECASTING ERROR; INPUT DATAS; INPUT-OUTPUT; INPUT-OUTPUT DATA; MODEL TRAINING; PRE-PROCESSING; RIVER FLOW FORECASTING; ROOT MEAN SQUARE ERRORS; STATISTICAL TECHNIQUES; TIME STEP;

EID: 78751663789     PISSN: 01447815     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (11)

References (19)
  • 1
    • 0034174280 scopus 로고    scopus 로고
    • Artificial neural networks in hydrology. I: Preliminary concepts
    • ASCE Task Committee
    • ASCE Task Committee (2000a) Artificial neural networks in hydrology. I: preliminary concepts. J. Hydrol. Engng ASCE 5, 115-123.
    • (2000) J. Hydrol. Engng ASCE , vol.5 , pp. 115-123
  • 2
    • 0034174396 scopus 로고    scopus 로고
    • Artificial neural networks in hydrology. II: Hydrologic applications
    • ASCE Task Committee
    • ASCE Task Committee (2000b) Artificial neural networks in hydrology. II: hydrologic applications. J. Hydrol. Engng ASCE 5, 124-135.
    • (2000) J. Hydrol. Engng ASCE , vol.5 , pp. 124-135
  • 3
    • 0034746068 scopus 로고    scopus 로고
    • Training artificial neural networks to perform rainfall disaggregation
    • Burian, S. J., Durrans, S. R., Nix, S. J. & Pitt, R. E. (2001) Training artificial neural networks to perform rainfall disaggregation. J. Hydrol. Engng ASCE 6,43-51.
    • (2001) J. Hydrol. Engng ASCE , vol.6 , pp. 43-51
    • Burian, S.J.1    Durrans, S.R.2    Nix, S.J.3    Pitt, R.E.4
  • 4
    • 33746834358 scopus 로고    scopus 로고
    • Identification of support vector machines for runoff modelling
    • Bray, M. & Han, D. (2004) Identification of support vector machines for runoff modelling. J. Hydroinf. 6,265-280.
    • (2004) J. Hydroinf. , vol.6 , pp. 265-280
    • Bray, M.1    Han, D.2
  • 5
    • 0033512986 scopus 로고    scopus 로고
    • A comparisons of artificial networks flow forecasting
    • Dawson, C. W. & Wilby, R. B. (1999) A comparisons of artificial networks flow forecasting. Hydrol. Earth System Sci. 3, 529-540.
    • (1999) Hydrol. Earth System Sci. , vol.3 , pp. 529-540
    • Dawson, C.W.1    Wilby, R.B.2
  • 6
    • 0036698154 scopus 로고    scopus 로고
    • Evaluation of artificial neural network techniques for flow forecasting in river Yangtze, China
    • Dawson, C. W., Harpharan, C, Wilby, R. B. & Chen, Y. (2002) Evaluation of artificial neural network techniques for flow forecasting in river Yangtze, China. Hydrol. Earth System Sci. 6,619-626.
    • (2002) Hydrol. Earth System Sci. , vol.6 , pp. 619-626
    • Dawson, C.W.1    Harpharan, C.2    Wilby, R.B.3    Chen, Y.4
  • 8
    • 0038502200 scopus 로고    scopus 로고
    • Artificial neural networks for streamflow prediction
    • Dolling, O. R. & Veras, E. A. (2002) Artificial neural networks for streamflow prediction. J. Hydraul. Res. 40,547-554.
    • (2002) J. Hydraul. Res. , vol.40 , pp. 547-554
    • Dolling, O.R.1    Veras, E.A.2
  • 10
    • 0004063090 scopus 로고
    • Macmillan, Englewood Cliffs, New Jersey, USA
    • Haykin, S. (1994) Neural Networks. Macmillan, Englewood Cliffs, New Jersey, USA.
    • (1994) Neural Networks
    • Haykin, S.1
  • 11
    • 0034641121 scopus 로고    scopus 로고
    • River flow prediction using artificial neural networks: Generalization beyond the calibration range
    • Imrie, C. E., Durucan, S. & Korre, A. (2000) River flow prediction using artificial neural networks: generalization beyond the calibration range. J. Hydrol. 233, 138-153.
    • (2000) J. Hydrol. , vol.233 , pp. 138-153
    • Imrie, C.E.1    Durucan, S.2    Korre, A.3
  • 13
    • 1642497522 scopus 로고    scopus 로고
    • River flow modeling using artificial neural networks
    • Kisi, O. (2004) River flow modeling using artificial neural networks. J. Hydrol. Engng ASCE 9(1), 60-63.
    • (2004) J. Hydrol. Engng ASCE , vol.9 , Issue.1 , pp. 60-63
    • Kisi, O.1
  • 14
    • 0033957764 scopus 로고    scopus 로고
    • Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications
    • Maier, H. R. & Dandy, G. C. (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Modeling & Software 15,101-124.
    • (2000) Environ. Modeling & Software , vol.15 , pp. 101-124
    • Maier, H.R.1    Dandy, G.C.2
  • 16
    • 0030201218 scopus 로고    scopus 로고
    • Effect of data normalization on neural network training
    • Shanker, M, Hu, M. Y. & Hung, M. S. (1996) Effect of data normalization on neural network training. Omega, Int. J. Manage Sci. 24,385-397.
    • (1996) Omega, Int. J. Manage Sci. , vol.24 , pp. 385-397
    • Shanker, M.1    Hu, M.Y.2    Hung, M.S.3
  • 17
    • 27644448548 scopus 로고    scopus 로고
    • Simulation of flood flow in a river system using artificial neural networks
    • Shrestha, R. R., Theobald, S. & Nestmann F. (2005) Simulation of flood flow in a river system using artificial neural networks. Hydrol. Earth System Sci. 9(4), 313-321.
    • (2005) Hydrol. Earth System Sci. , vol.9 , Issue.4 , pp. 313-321
    • Shrestha, R.R.1    Theobald, S.2    Nestmann, F.3
  • 18
    • 0038546820 scopus 로고    scopus 로고
    • Estimating actual evapotranspiration from limited climatic data using neural network computing technique
    • Sudheer, K. P., Gosain, A. K. & Ramasastri, K. S. (2003) Estimating actual evapotranspiration from limited climatic data using neural network computing technique. J. Irrig. Drain. Engng 129(3), 214-218.
    • (2003) J. Irrig. Drain. Engng , vol.129 , Issue.3 , pp. 214-218
    • Sudheer, K.P.1    Gosain, A.K.2    Ramasastri, K.S.3
  • 19
    • 0036330344 scopus 로고    scopus 로고
    • Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data
    • Zhang, Y., Pulliainen, J., Koponen, S.& Hallikainen, M. (2002) Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data. Remote Sens. Environ. 81, 327-336.
    • (2002) Remote Sens. Environ. , vol.81 , pp. 327-336
    • Zhang, Y.1    Pulliainen, J.2    Koponen, S.3    Hallikainen, M.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.