메뉴 건너뛰기




Volumn 8, Issue 17, 2008, Pages 2949-2957

Suitability of artificial neural network in daily flow forecasting

Author keywords

Daily river flows; L M and CG algorithm; Neural networks

Indexed keywords

ARTIFICIAL NEURAL NETWORK ALGORITHM; BP (BACK PROPAGATION) ALGORITHM; FLOW ESTIMATION; FLOW FORECASTING; MULTI-LAYER PERCEPTRONS; RIVER FLOW; RIVER FLOW FORECASTING; STREAM-FLOW EVENTS;

EID: 67149100967     PISSN: 18125654     EISSN: 18125662     Source Type: Journal    
DOI: 10.3923/jas.2008.2949.2957     Document Type: Article
Times cited : (4)

References (36)
  • 1
    • 0034210373 scopus 로고    scopus 로고
    • Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events
    • Abebe, A.J., D.P. Solomatine and R.G.W. Venneker, 2000. Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events. Hydrol. Sci. J., 45: 425-436.
    • (2000) Hydrol. Sci. J. , vol.45 , pp. 425-436
    • Abebe, A.J.1    Solomatine, D.P.2    Venneker, R.G.W.3
  • 2
    • 0034174280 scopus 로고    scopus 로고
    • Artificial neural networks in Hydrology I
    • ASCE Task Committee
    • ASCE Task Committee, 2000a. Artificial neural networks in Hydrology I. J. Hydro. Eng., ASCE, 5: 115-123.
    • (2000) J. Hydro. Eng., ASCE , vol.5 , pp. 115-123
  • 3
    • 0034174396 scopus 로고    scopus 로고
    • Artificial neural networks in Hydrology II
    • ASCE Task Committee
    • ASCE Task Committee, 2000b. Artificial neural networks in Hydrology II. J. Hydro. Eng. ASCE, 5: 124-132.
    • (2000) J. Hydro. Eng. ASCE , vol.5 , pp. 124-132
  • 4
    • 0036719845 scopus 로고    scopus 로고
    • Real-time recurrent learning neural network for stream-flow forecasting
    • Chang, F.J., L.C. Chang and H.L. Huang, 2002. Real-time recurrent learning neural network for stream-flow forecasting. Hydrol. Processes, 16: 2577-2588.
    • (2002) Hydrol. Processes , vol.16 , pp. 2577-2588
    • Chang, F.J.1    Chang, L.C.2    Huang, H.L.3
  • 5
    • 0842349306 scopus 로고    scopus 로고
    • A two-step-ahead recurrent neural network for stream-flow forecasting
    • Chang, L.C., F.J. Chang and Y.M. Chiang, 2004. A two-step-ahead recurrent neural network for stream-flow forecasting. Hydrol. Processes, 18: 81-92.
    • (2004) Hydrol. Processes , vol.18 , pp. 81-92
    • Chang, L.C.1    Chang, F.J.2    Chiang, Y.M.3
  • 6
    • 0026882084 scopus 로고
    • Conjugate gradient algorithm for efficient training of artificial neural networks
    • Charalambous, C., 1992. Conjugate gradient algorithm for efficient training of artificial neural networks. IEEE. Proc., 139: 301-310.
    • (1992) IEEE. Proc. , vol.139 , pp. 301-310
    • Charalambous, C.1
  • 7
    • 1842426595 scopus 로고    scopus 로고
    • Comparison of static-feedforward and dynamic-fedback neural networks for rainfall-runoff modeling
    • Chiang, Y.M., L.C. Chang and F.J. Chang, 2004. Comparison of static-feedforward and dynamic-fedback neural networks for rainfall-runoff modeling. J. Hydrol., 290: 297-311.
    • (2004) J. Hydrol. , vol.290 , pp. 297-311
    • Chiang, Y.M.1    Chang, L.C.2    Chang, F.J.3
  • 8
    • 23044443211 scopus 로고    scopus 로고
    • Application of the generalized regression neural networks to intermittent flow forecasting and estimation
    • Cigizoglu, H.K., 2005. Application of the generalized regression neural networks to intermittent flow forecasting and estimation. ASCE J. Hydrol. Eng., 10: 336-341.
    • (2005) ASCE J. Hydrol. Eng. , vol.10 , pp. 336-341
    • Cigizoglu, H.K.1
  • 9
    • 28944434082 scopus 로고    scopus 로고
    • Methods to improve the neural network performance in suspended sediment estimation
    • Cigizoglu, H.K. and O. Kisi, 2006. Methods to improve the neural network performance in suspended sediment estimation. J. Hydrol., 317: 221-238.
    • (2006) J. Hydrol. , vol.317 , pp. 221-238
    • Cigizoglu, H.K.1    Kisi, O.2
  • 10
    • 0031932822 scopus 로고    scopus 로고
    • Neural networks to assess influence of changing seasonal climates in modifying discharge, dissolved organic carbon and nitrogen export in eastern Canadian rivers
    • Clair, T.A. and J.M. Ehrman, 1998. Neural networks to assess influence of changing seasonal climates in modifying discharge, dissolved organic carbon and nitrogen export in eastern Canadian rivers. Water Resour. Res., 34: 447-455.
    • (1998) Water Resour. Res. , vol.34 , pp. 447-455
    • Clair, T.A.1    Ehrman, J.M.2
  • 11
    • 26444574880 scopus 로고    scopus 로고
    • Downscaling precipitation and temperature with temporal neural networks
    • Coulibaly, P., Y.B. Dibike and F. Anctil, 2005. Downscaling precipitation and temperature with temporal neural networks. J. Hydrometeorol., 6: 483-496.
    • (2005) J. Hydrometeorol. , vol.6 , pp. 483-496
    • Coulibaly, P.1    Dibike, Y.B.2    Anctil, F.3
  • 12
    • 34250818666 scopus 로고    scopus 로고
    • Comparison of neural network methods for infilling missing daily weather records
    • Coulibaly, P. and N.D. Evora, 2007. Comparison of neural network methods for infilling missing daily weather records. J. Hydrol., 341: 27-41.
    • (2007) J. Hydrol. , vol.341 , pp. 27-41
    • Coulibaly, P.1    Evora, N.D.2
  • 13
    • 33645989739 scopus 로고    scopus 로고
    • Temporal neural networks for downscaling climate variability and extremes
    • Dibike, Y.B. and P. Coulibaly, 2006. Temporal neural networks for downscaling climate variability and extremes. Neural Networks, 19: 135-144.
    • (2006) Neural Networks , vol.19 , pp. 135-144
    • Dibike, Y.B.1    Coulibaly, P.2
  • 14
    • 0037562812 scopus 로고    scopus 로고
    • Feed forward neural networks modeling for K-P interactions
    • (Elsevier)
    • El-Bakyr, M.Y., 2003. Feed forward neural networks modeling for K-P interactions. Chaos, Solit. Fractals, 18: 995-1000 (Elsevier).
    • (2003) Chaos, Solit. Fractals , vol.18 , pp. 995-1000
    • El-Bakyr, M.Y.1
  • 15
    • 0027007868 scopus 로고
    • Rainfall forecasting in space and time using a neural network
    • French, M.N, W.F. Krajewski and R.R. Cuykendal, 1992. Rainfall forecasting in space and time using a neural network. J. Hydrol., 137: 1-37.
    • (1992) J. Hydrol. , vol.137 , pp. 1-37
    • French, M.N.1    Krajewski, W.F.2    Cuykendal, R.R.3
  • 16
    • 0028974862 scopus 로고
    • An integrated software environment for real-time use of a distributed hydrologic model
    • Garrote, L. and R.L. Bras, 1995. An integrated software environment for real-time use of a distributed hydrologic model. J. Hydrol., 167: 307-326.
    • (1995) J. Hydrol. , vol.167 , pp. 307-326
    • Garrote, L.1    Bras, R.L.2
  • 17
    • 20844456071 scopus 로고    scopus 로고
    • Improving generalization of artificial neural networks in rainfall runoff modeling
    • Giustolisi, O. and D. Laucelli, 2005. Improving generalization of artificial neural networks in rainfall runoff modeling. Hydrol. Sci. J., 50: 439-457.
    • (2005) Hydrol. Sci. J. , vol.50 , pp. 439-457
    • Giustolisi, O.1    Laucelli, D.2
  • 18
    • 0027149406 scopus 로고
    • Predicting runoff from rainfall using neural networks
    • Proceeding of Engineering Hydrol. 1993 ASCE, New York
    • Halff, A.H., H.M. Halff and M. Azmoodeh, 1993. Predicting runoff from rainfall using neural networks. Proceeding of Engineering Hydrol. 1993 ASCE, New York, 760-765.
    • (1993) , pp. 760-765
    • Halff, A.H.1    Halff, H.M.2    Azmoodeh, M.3
  • 20
    • 13244251543 scopus 로고    scopus 로고
    • Self organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modelling and analysis
    • Hsu, K., H.V. Gupta, Gao, S. Sorooshian and B. Imam, 2002. Self organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modelling and analysis. J. Hydrol., 38: 1-17.
    • (2002) J. Hydrol. , vol.38 , pp. 1-17
    • Hsu, K.1    Gupta, H.V.2    Gao3    Sorooshian, S.4    Imam, B.5
  • 21
    • 0035472003 scopus 로고    scopus 로고
    • River flow time series prediction with a range-dependent neural network
    • Hu, T.S., K.C. Lam and S.T. Ng, 2001. River flow time series prediction with a range-dependent neural network. Hydrol. Sci. J., 46: 729-745.
    • (2001) Hydrol. Sci. J. , vol.46 , pp. 729-745
    • Hu, T.S.1    Lam, K.C.2    Ng, S.T.3
  • 22
    • 0034641121 scopus 로고    scopus 로고
    • River flow prediction using artificial neural networks: Generalisation beyond the calibration range
    • Imrie, C.E., S. Durucan and A. Korre, 2000. River flow prediction using artificial neural networks: Generalisation 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
  • 23
    • 1642497522 scopus 로고    scopus 로고
    • River flow modeling using artificial neural networks
    • Kisi, O., 2004. River flow modeling using artificial neural networks. J. Hydrol. Eng., 9: 60-63.
    • (2004) J. Hydrol. Eng. , vol.9 , pp. 60-63
    • Kisi, O.1
  • 24
    • 2542447559 scopus 로고    scopus 로고
    • River flow forecasting using recurrent neural networks
    • Kumar, D.N., K.S. Raju and T. Sathish, 2004. River flow forecasting using recurrent neural networks. Water Resour. Mange., 18: 143-161.
    • (2004) Water Resour. Mange. , vol.18 , pp. 143-161
    • Kumar, D.N.1    Raju, K.S.2    Sathish, T.3
  • 25
    • 1642387025 scopus 로고    scopus 로고
    • A non-linear rainfall-runoff model using radial basis function network
    • Lin, G.F. and L.H. Chen, 2004. A non-linear rainfall-runoff model using radial basis function network. J. Hydrol., 289: 1-8.
    • (2004) J. Hydrol. , vol.289 , pp. 1-8
    • Lin, G.F.1    Chen, L.H.2
  • 26
    • 0034737033 scopus 로고    scopus 로고
    • A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting
    • Luk, K.C., J.E. Ball and A. Sharma, 2000. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J. Hydrol., 227: 56-65.
    • (2000) J. Hydrol. , vol.227 , pp. 56-65
    • Luk, K.C.1    Ball, J.E.2    Sharma, A.3
  • 27
    • 0035104376 scopus 로고    scopus 로고
    • An application of artificial neural networks for rainfall forecasting
    • Luk, K.C., J.E. Ball and A. Sharma, 2001. An application of artificial neural networks for rainfall forecasting. Math. Computer Modell., 33: 683-693.
    • (2001) Math. Computer Modell. , vol.33 , pp. 683-693
    • Luk, K.C.1    Ball, J.E.2    Sharma, A.3
  • 28
    • 0033957764 scopus 로고    scopus 로고
    • Neural networks for prediction and forecasting of water resources variables: A review of modeling issues and applications
    • Maier, H.R. and G.C. Dandy, 2000. Neural networks for prediction and forecasting of water resources variables: A review of modeling issues and applications. Environ. Model. Software, 15: 101-123.
    • (2000) Environ. Model. Software , vol.15 , pp. 101-123
    • Maier, H.R.1    Dandy, G.C.2
  • 29
    • 27644537224 scopus 로고    scopus 로고
    • Prediction of flow characteristics using multiple regression and neural networks: A case study in Zimbabwe
    • Mazvimavi, D., A.M.J. Maijerink, H.H.G. Savenije and A. Stein, 2005. Prediction of flow characteristics using multiple regression and neural networks: A case study in Zimbabwe. Phys. Chem. Earth, 30: 639-647.
    • (2005) Phys. Chem. Earth , vol.30 , pp. 639-647
    • Mazvimavi, D.1    Maijerink, A.M.J.2    Savenije, H.H.G.3    Stein, A.4
  • 32
    • 0347135926 scopus 로고    scopus 로고
    • Modeling of the daily rainfall-runoff relationship with artificial neural network
    • Rajurkara, M.P., U.C. Kothyarib and U.C. Chaubec, 2004. Modeling of the daily rainfall-runoff relationship with artificial neural network. J. Hydrol., 285: 93-113.
    • (2004) J. Hydrol. , vol.285 , pp. 93-113
    • Rajurkara, M.P.1    Kothyarib, U.C.2    Chaubec, U.C.3
  • 33
    • 10644287862 scopus 로고    scopus 로고
    • Artificial neural network technique for rainfall forecasting applied to the Saõ Paulo region
    • Rami'rez, M.C.P., H.F.C. Velho and N.J. Ferreira, 2005. Artificial neural network technique for rainfall forecasting applied to the Saõ Paulo region. J. Hydrol., 301: 146-162.
    • (2005) J. Hydrol. , vol.301 , pp. 146-162
    • Rami'rez, M.C.P.1    Velho, H.F.C.2    Ferreira, N.J.3
  • 34
    • 4644296256 scopus 로고    scopus 로고
    • Predicting catchment flow in a semi-arid region via an artificial neural network technique
    • Riad, S., J. Mania, L. Bouchaou and Y. Najjar, 2004. Predicting catchment flow in a semi-arid region via an artificial neural network technique. Hydrol. Processes J., 18: 2387-2393.
    • (2004) Hydrol. Processes J. , vol.18 , pp. 2387-2393
    • Riad, S.1    Mania, J.2    Bouchaou, L.3    Najjar, Y.4
  • 36
    • 0037197571 scopus 로고    scopus 로고
    • A data-driven algorithm for constructing artificial neural network rainfall-runoff models
    • Sudheer, K.P., A.K. Gosain and K.S. Ramasastri, 2002. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol. Process., 16: 1325-1330.
    • (2002) Hydrol. Process. , vol.16 , pp. 1325-1330
    • Sudheer, K.P.1    Gosain, A.K.2    Ramasastri, K.S.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.