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Volumn 22, Issue 16, 2008, Pages 3091-3101

Neural network time series prediction of environmental variables in a small upland headwater in NE Scotland

Author keywords

Neural network; Stream flow; Time series prediction; Weather monitoring

Indexed keywords

FLOW OF WATER; FORECASTING; IMAGE CLASSIFICATION; LANDFORMS; NEON; STANDARDS; STREAM FLOW; TIME SERIES ANALYSIS;

EID: 49249108312     PISSN: 08856087     EISSN: 10991085     Source Type: Journal    
DOI: 10.1002/hyp.6895     Document Type: Article
Times cited : (9)

References (25)
  • 1
    • 34248202148 scopus 로고    scopus 로고
    • Artificial neural network model for synthetic streamflow generation
    • Ahmed JA, Sarma AK. 2007. Artificial neural network model for synthetic streamflow generation. Water Resources Management 21: 1015-1029.
    • (2007) Water Resources Management , vol.21 , pp. 1015-1029
    • Ahmed, J.A.1    Sarma, A.K.2
  • 4
    • 0028965995 scopus 로고
    • A recurrent network for modelling noisy temporal sequences
    • Bulsari AB, Saxén H. 1995. A recurrent network for modelling noisy temporal sequences. Neurocomputing 7: 29-40.
    • (1995) Neurocomputing , vol.7 , pp. 29-40
    • Bulsari, A.B.1    Saxén, H.2
  • 5
    • 0042502828 scopus 로고
    • Analysing the past and managing the future using neural networks
    • Canarelli P. 1995. Analysing the past and managing the future using neural networks. Futures 27(3): 325-338.
    • (1995) Futures , vol.27 , Issue.3 , pp. 325-338
    • Canarelli, P.1
  • 6
    • 0842349306 scopus 로고    scopus 로고
    • A two-step ahead recurrent neural network for stream-flow forecasting
    • Chang L-C, Chang F-J, Chiang Y-M. 2004. A two-step ahead recurrent neural network for stream-flow forecasting. Hydrological Processes 18: 81-92.
    • (2004) Hydrological Processes , vol.18 , pp. 81-92
    • Chang, L.-C.1    Chang, F.-J.2    Chiang, Y.-M.3
  • 7
    • 0003216105 scopus 로고    scopus 로고
    • Noninvertibility and resonance in discrete-time neural networks for time-series processing
    • Gicquel N, Anderson JS, Kevrekidis IG. 1998. Noninvertibility and resonance in discrete-time neural networks for time-series processing. Physics Letters A 238: 8-18.
    • (1998) Physics Letters A , vol.238 , pp. 8-18
    • Gicquel, N.1    Anderson, J.S.2    Kevrekidis, I.G.3
  • 8
    • 0029223565 scopus 로고
    • Back-propagation neural networks for modelling complex systems
    • Goh ATC. 1995. Back-propagation neural networks for modelling complex systems. Artificial Intelligence in Engineering 9: 143-151.
    • (1995) Artificial Intelligence in Engineering , vol.9 , pp. 143-151
    • Goh, A.T.C.1
  • 9
  • 10
    • 1542287371 scopus 로고    scopus 로고
    • Identification of physical processes inherent in artificial neural network rainfall runoff models
    • Jain A, Sudheer KP, Srinivasulu S. 2004. Identification of physical processes inherent in artificial neural network rainfall runoff models. Hydrological Processes 18: 571-581.
    • (2004) Hydrological Processes , vol.18 , pp. 571-581
    • Jain, A.1    Sudheer, K.P.2    Srinivasulu, S.3
  • 11
    • 0036086875 scopus 로고    scopus 로고
    • Observational learning algorithm for an ensemble of neural networks
    • Jang M, Cho S. 2002. Observational learning algorithm for an ensemble of neural networks. Pattern Analysis and Applications 5: 154-167.
    • (2002) Pattern Analysis and Applications , vol.5 , pp. 154-167
    • Jang, M.1    Cho, S.2
  • 12
    • 30444441291 scopus 로고    scopus 로고
    • Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction
    • Jeong D-I, Kim Y-O. 2005. Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction. Hydrological Processes 19: 3819-3835.
    • (2005) Hydrological Processes , vol.19 , pp. 3819-3835
    • Jeong, D.-I.1    Kim, Y.-O.2
  • 13
    • 0011180615 scopus 로고
    • Sequential adaptation of radial basis function neural networks and its application to time-series prediction
    • San Mateo, CA, April, Lippmann RP, Moody J, Touretzky DS eds, Morgan Kaufmann
    • Kadirkamanathan V, Niranjan M, Fallside F. 1991. Sequential adaptation of radial basis function neural networks and its application to time-series prediction. In Advances in Neural Information Processing (NIPS-90), San Mateo, CA, April 1991, Lippmann RP, Moody J, Touretzky DS (eds). Morgan Kaufmann.
    • (1991) Advances in Neural Information Processing (NIPS-90)
    • Kadirkamanathan, V.1    Niranjan, M.2    Fallside, F.3
  • 14
    • 0032584970 scopus 로고    scopus 로고
    • Time-delay recurrent neural network for temporal correlations and prediction
    • Kim S-S. 1998. Time-delay recurrent neural network for temporal correlations and prediction. Neurocomputing 20: 253-263.
    • (1998) Neurocomputing , vol.20 , pp. 253-263
    • Kim, S.-S.1
  • 15
    • 0030506806 scopus 로고    scopus 로고
    • Computational study on the neural mechanism of sequential pattern memory
    • Morita M. 1996a. Computational study on the neural mechanism of sequential pattern memory. Cognitive Brain Research 5: 137-146.
    • (1996) Cognitive Brain Research , vol.5 , pp. 137-146
    • Morita, M.1
  • 16
    • 0030296406 scopus 로고    scopus 로고
    • Memory and learning of sequential patterns by nonmonotone neural networks
    • Morita M. 1996h. Memory and learning of sequential patterns by nonmonotone neural networks. Neural Networks 9(8): 1477-1489.
    • (1996) Neural Networks , vol.9 , Issue.8 , pp. 1477-1489
    • Morita, M.1
  • 19
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323: 533-536.
    • (1986) Nature , vol.323 , pp. 533-536
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 20
    • 49249134580 scopus 로고    scopus 로고
    • Singh VP, Frevert DK. 2002. Mathematical Models of Small Watershed Hydrology and Applications. Water Resources Publications: Littleton; 792.
    • Singh VP, Frevert DK. 2002. Mathematical Models of Small Watershed Hydrology and Applications. Water Resources Publications: Littleton; 792.
  • 23
    • 0032090662 scopus 로고    scopus 로고
    • Forecasting SandP 500 stock index futures with a hybrid Al system
    • Tsaih R, Hsu Y, Lai CC. 1998. Forecasting SandP 500 stock index futures with a hybrid Al system. Decision Support Systems 23: 161-174.
    • (1998) Decision Support Systems , vol.23 , pp. 161-174
    • Tsaih, R.1    Hsu, Y.2    Lai, C.C.3
  • 24
    • 0033019602 scopus 로고    scopus 로고
    • Short term stream flow forecasting using artificial neural networks
    • Zealand CM, Burn DH, Simonovic SP. 1999. Short term stream flow forecasting using artificial neural networks. Journal of Hydrology 214: 32-48.
    • (1999) Journal of Hydrology , vol.214 , pp. 32-48
    • Zealand, C.M.1    Burn, D.H.2    Simonovic, S.P.3
  • 25
    • 0343052623 scopus 로고    scopus 로고
    • Rainfall estimation using artificial neural network group
    • Zhang M, Fulcher J, Scofield RA. 1997. Rainfall estimation using artificial neural network group. Neurocomputing 16: 97-115.
    • (1997) Neurocomputing , vol.16 , pp. 97-115
    • Zhang, M.1    Fulcher, J.2    Scofield, R.A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.