메뉴 건너뛰기




Volumn 291, Issue 1-2, 2004, Pages 52-66

A neuro-fuzzy computing technique for modeling hydrological time series

Author keywords

Fuzzy inference system; Fuzzy logic; Hydrological modeling; Neural networks; Time series modeling

Indexed keywords

ARTIFICIAL INTELLIGENCE; BRAIN MODELS; EXPERT SYSTEMS; HYDROLOGY; RISK ASSESSMENT; SIGNAL PROCESSING; TIME SERIES ANALYSIS;

EID: 1942490118     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2003.12.010     Document Type: Article
Times cited : (568)

References (43)
  • 1
    • 84979183578 scopus 로고
    • A critique of current methods of hydrologic systems investigations
    • Amorocho J., Brandstetter A. A critique of current methods of hydrologic systems investigations. Eos Transactions of AGU. 45:1971;307-321.
    • (1971) Eos Transactions of AGU , vol.45 , pp. 307-321
    • Amorocho, J.1    Brandstetter, A.2
  • 2
    • 0033105287 scopus 로고    scopus 로고
    • Model selection in neural networks
    • Anders U., Korn O. Model selection in neural networks. Neural Networks. 12:1999;309-323.
    • (1999) Neural Networks , vol.12 , pp. 309-323
    • Anders, U.1    Korn, O.2
  • 3
    • 0034174280 scopus 로고    scopus 로고
    • Artificial neural networks in hydrology-I: Preliminary concepts
    • ASCE Task Committee. Artificial neural networks in hydrology-I: Preliminary concepts. Journal of Hydrologic Engineering, ASCE. 5:(2):2000;115-123.
    • (2000) Journal of Hydrologic Engineering, ASCE , vol.5 , Issue.2 , pp. 115-123
  • 4
    • 0034174396 scopus 로고    scopus 로고
    • Artificial neural networks in hydrology-II: Hydrologic applications
    • ASCE Task Committee. Artificial neural networks in hydrology-II: Hydrologic applications. Journal of Hydrologic Engineering, ASCE. 5:(2):2000;124-137.
    • (2000) Journal of Hydrologic Engineering, ASCE , vol.5 , Issue.2 , pp. 124-137
  • 7
    • 0039988139 scopus 로고    scopus 로고
    • Time series forecasting with neural networks: A comparative study using the airline data
    • Faraway J., Chatfield C. Time series forecasting with neural networks: a comparative study using the airline data. Applied Statistics. 47:(Part 2):1998;231-250.
    • (1998) Applied Statistics , vol.47 , Issue.PART 2 , pp. 231-250
    • Faraway, J.1    Chatfield, C.2
  • 8
    • 0001573780 scopus 로고    scopus 로고
    • Comments on the use of artificial neural networks for the prediction of water quality parameters by H.R. Maier and G.C. dandy
    • Fortin V., Quarda T.B.M.J., Bobee B. Comments on the use of artificial neural networks for the prediction of water quality parameters by H.R. Maier and G.C. dandy. Water Resources Research. 33:(10):1997;2423-2424.
    • (1997) Water Resources Research , vol.33 , Issue.10 , pp. 2423-2424
    • Fortin, V.1    Quarda, T.B.M.J.2    Bobee, B.3
  • 10
    • 0024880831 scopus 로고
    • Multi layer feed forward networks are universal approximators
    • Hornik K., Stichcombe M., White H. Multi layer feed forward networks are universal approximators. Neural Networks. 2:1989;359-366.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stichcombe, M.2    White, H.3
  • 13
    • 0006994099 scopus 로고
    • A mathematical model for non-linear hydrologic systems
    • Jacoby S.L.S. A mathematical model for non-linear hydrologic systems. Journal of Geophysics Research. 71:(20):1966;4811-4824.
    • (1966) Journal of Geophysics Research , vol.71 , Issue.20 , pp. 4811-4824
    • Jacoby, S.L.S.1
  • 14
    • 0037340658 scopus 로고    scopus 로고
    • Comparative analysis of event based rainfall-runoff modeling techniques-deterministic, statistical, and artificial neural networks
    • Jain A., Indurthy S.K.V.P. Comparative analysis of event based rainfall-runoff modeling techniques-deterministic, statistical, and artificial neural networks. Journal of Hydrologic Engineering ASCE. 8:(2):2003;93-98.
    • (2003) Journal of Hydrologic Engineering ASCE , vol.8 , Issue.2 , pp. 93-98
    • Jain, A.1    Indurthy, S.K.V.P.2
  • 15
    • 0013366549 scopus 로고
    • Rule extraction using generalized neural networks
    • Volume for Artificial Intelligence
    • Jang J.-S.R. Rule extraction using generalized neural networks. In Proceedings of the fourth IFSA World Congress. 4:1991;82-86. Volume for Artificial Intelligence.
    • (1991) In Proceedings of the Fourth IFSA World Congress , vol.4 , pp. 82-86
    • Jang, J.-S.R.1
  • 17
    • 0029273384 scopus 로고
    • Neuro-fuzzy modeling and control
    • Jang J.-S.R., Sun C.-T. Neuro-fuzzy modeling and control. Proceedings IEEE. 83:(3):1995;378-406.
    • (1995) Proceedings IEEE , vol.83 , Issue.3 , pp. 378-406
    • Jang, J.-S.R.1    Sun, C.-T.2
  • 19
    • 0031222587 scopus 로고    scopus 로고
    • Determining inputs for neural network models of multivariate time series
    • Maier H.R., Dandy G.C. Determining inputs for neural network models of multivariate time series. Microcomputers in Civil Engineering. 12:1997;353-368.
    • (1997) Microcomputers in Civil Engineering , vol.12 , pp. 353-368
    • Maier, H.R.1    Dandy, G.C.2
  • 22
    • 0030159380 scopus 로고    scopus 로고
    • Artificial Neural networks as Rainfall-Runoff models
    • Minns A.W., Hall M.J. Artificial Neural networks as Rainfall-Runoff models. Hydrological Sciences Journal. 41:(3):1996;399-417.
    • (1996) Hydrological Sciences Journal , vol.41 , Issue.3 , pp. 399-417
    • Minns, A.W.1    Hall, M.J.2
  • 23
    • 0016985232 scopus 로고
    • A stable estimator for linear models, 1, theoretical development and Monte Carlo experiments
    • Natale L., Todini E. A stable estimator for linear models, 1, theoretical development and Monte Carlo experiments. Water Resources Research. 12:(4):1976;664-671.
    • (1976) Water Resources Research , vol.12 , Issue.4 , pp. 664-671
    • Natale, L.1    Todini, E.2
  • 24
    • 0016985128 scopus 로고
    • A stable estimator for linear models, 2. Real world hydrologic applications
    • Natale L., Todini E. A stable estimator for linear models, 2. Real world hydrologic applications. Water Resources Research. 12:(4):1976;672-676.
    • (1976) Water Resources Research , vol.12 , Issue.4 , pp. 672-676
    • Natale, L.1    Todini, E.2
  • 25
    • 1942464873 scopus 로고
    • M Sc. Thesis, Tempere Univeristy of Technology, Tampere, Finland.
    • Ojala, T., 1995 Neuro-Fuzzy systems in control. M Sc. Thesis, Tempere Univeristy of Technology, Tampere, Finland.
    • (1995) Neuro-Fuzzy Systems in Control
    • Ojala, T.1
  • 26
    • 0029413038 scopus 로고
    • Multivariate modeling of water resources time series using artificial neural networks
    • Raman H., Sunilkumar N. Multivariate modeling of water resources time series using artificial neural networks. Journal of hydrological sciences. 40:1995;145-163.
    • (1995) Journal of Hydrological Sciences , vol.40 , pp. 145-163
    • Raman, H.1    Sunilkumar, N.2
  • 27
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • Rumelhart D.E., Hinton G.E., Williams R.J. 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
  • 28
    • 0022111962 scopus 로고
    • Approaches to multivariate modeling of water resources time series
    • Salas J.D., Tabios Q.V., Bartolini P. Approaches to multivariate modeling of water resources time series. Water Resources Bullettin. 21:(4):1985;683-708.
    • (1985) Water Resources Bullettin , vol.21 , Issue.4 , pp. 683-708
    • Salas, J.D.1    Tabios, Q.V.2    Bartolini, P.3
  • 29
    • 0033381989 scopus 로고    scopus 로고
    • Applying soft computing approaches to river level forecasting
    • See L., Openshaw S. Applying soft computing approaches to river level forecasting. Hydrological Sciences Journal. 44:(5):2000;763-779.
    • (2000) Hydrological Sciences Journal , vol.44 , Issue.5 , pp. 763-779
    • See, L.1    Openshaw, S.2
  • 30
    • 0000859675 scopus 로고
    • Cross validation choice and assessment of statistical predictions
    • Stone M. Cross validation choice and assessment of statistical predictions. Journal of Royal Statistical Society, B. 36:1974;44-47.
    • (1974) Journal of Royal Statistical Society, B , vol.36 , pp. 44-47
    • Stone, M.1
  • 31
    • 0034451729 scopus 로고    scopus 로고
    • Machine supported development of fuzzy-flood forecast systems
    • Potsdam, PIK report Nr.65, Axel Bronstert, Christine Bismuth, Lucas Menzel (Ed.), Reprint of Proceedings, 2
    • Stuber, M., Gemmar, P., Greving, M., 2000 Machine supported development of fuzzy-flood forecast systems, European Conference on Advances in Flood Research, Potsdam, PIK report Nr.65, Axel Bronstert, Christine Bismuth, Lucas Menzel (Ed.), Reprint of Proceedings, 2, 504-515.
    • (2000) European Conference on Advances in Flood Research , pp. 504-515
    • Stuber, M.1    Gemmar, P.2    Greving, M.3
  • 33
    • 0037197571 scopus 로고    scopus 로고
    • A data-driven algorithm for constructing artificial neural network rainfall-runoff models
    • Sudheer K.P., Gosain A.K., Ramasastri K.S. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrological Processes. 16:2002;1325-1330.
    • (2002) Hydrological Processes , vol.16 , pp. 1325-1330
    • Sudheer, K.P.1    Gosain, A.K.2    Ramasastri, K.S.3
  • 35
    • 0027544110 scopus 로고
    • A fuzzy-logic based approach to qualitative modeling
    • Sugeno M., Yasukawa T. A fuzzy-logic based approach to qualitative modeling. IEEE Transactions On Fuzzy Systems. 1:(1):1993;7-31.
    • (1993) IEEE Transactions on Fuzzy Systems , vol.1 , Issue.1 , pp. 7-31
    • Sugeno, M.1    Yasukawa, T.2
  • 36
    • 45449126257 scopus 로고
    • Structure identification of fuzzy model
    • Sugeno M., Kang G.T. Structure identification of fuzzy model. Fuzzy Sets and Systems. 28:1988;15-33.
    • (1988) Fuzzy Sets and Systems , vol.28 , pp. 15-33
    • Sugeno, M.1    Kang, G.T.2
  • 37
    • 0021892282 scopus 로고
    • Fuzzy identification of systems and its application to modeling and control
    • Takagi T., Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man and Cybernetics. 15:(1):1985;116-132.
    • (1985) IEEE Transactions on Systems, Man and Cybernetics , vol.15 , Issue.1 , pp. 116-132
    • Takagi, T.1    Sugeno, M.2
  • 39
    • 0002906650 scopus 로고
    • An approach to fuzzy reasoning method
    • M.M. Gupta, R.K. Ragade, & R.R. Yager. Amsterdam: North-Holland
    • Tsukamoto Y. An approach to fuzzy reasoning method. Gupta M.M., Ragade R.K., Yager R.R. Advances in Fuzzy Set Theory and Application. 1979;137-149 North-Holland, Amsterdam.
    • (1979) Advances in Fuzzy Set Theory and Application , pp. 137-149
    • Tsukamoto, Y.1
  • 40
    • 0035340544 scopus 로고    scopus 로고
    • A nonlinear combination of the forecasts of rainfall-runoff models by the first order Takagi-Sugeno fuzzy system
    • Xiong L.H., Shamseldin A.Y., O'Connor K.M. A nonlinear combination of the forecasts of rainfall-runoff models by the first order Takagi-Sugeno fuzzy system. Journal of Hydrology. 245:(1-4):2001;196-217.
    • (2001) Journal of Hydrology , vol.245 , Issue.14 , pp. 196-217
    • Xiong, L.H.1    Shamseldin, A.Y.2    O'Connor, K.M.3
  • 42
    • 0000251270 scopus 로고
    • Comparison between fuzzy reasoning and neural network method to forecast runoff discharge
    • Zhu M.-L., Fujita M. Comparison between fuzzy reasoning and neural network method to forecast runoff discharge. Journal of Hydroscience and Hydraulic Engineering. 12:(2):1994;131-141.
    • (1994) Journal of Hydroscience and Hydraulic Engineering , vol.12 , Issue.2 , pp. 131-141
    • Zhu, M.-L.1    Fujita, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.