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Volumn 7, Issue 22, 2007, Pages 3451-3459

Application of fuzzy systems and artificial neural networks for flood forecasting

Author keywords

Artificial neural networks; Dez reservoir; Flood forecasting; Flood hazard; Fuzzy logic; Reservoir inflow

Indexed keywords

COMPUTER CIRCUITS; DEEP NEURAL NETWORKS; FLOOD CONTROL; FLOODS; FORECASTING; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; HAZARDS; NEURAL NETWORKS; RESERVOIRS (WATER); WEATHER FORECASTING;

EID: 36849063708     PISSN: 18125654     EISSN: 18125662     Source Type: Journal    
DOI: 10.3923/jas.2007.3451.3459     Document Type: Article
Times cited : (19)

References (33)
  • 1
    • 28344455955 scopus 로고    scopus 로고
    • An artificial neural network model for generating hydrograph from hydro-meteorological Parameters
    • Ahmad, S. and S.P. Simonovic, 2005. An artificial neural network model for generating hydrograph from hydro-meteorological Parameters. J. Hydrol., 315: 236-251.
    • (2005) J. Hydrol , vol.315 , pp. 236-251
    • Ahmad, S.1    Simonovic, S.P.2
  • 2
    • 0034174280 scopus 로고    scopus 로고
    • Artificial neural networks in hydrology. É: Preliminary concepts
    • ASCE Task Committee
    • ASCE Task Committee, 2000. Artificial neural networks in hydrology. É: Preliminary concepts. J. Hydrol. Eng., 5: 115-123.
    • (2000) J. Hydrol. Eng , vol.5 , pp. 115-123
  • 3
    • 12144264770 scopus 로고    scopus 로고
    • Neural networks and m5 model trees in modeling water level-discharge relationship
    • Bhattacharya, B. and D.P. Solomatine, 2005. Neural networks and m5 model trees in modeling water level-discharge relationship. Neuro Comput., 63: 381-396.
    • (2005) Neuro Comput , vol.63 , pp. 381-396
    • Bhattacharya, B.1    Solomatine, D.P.2
  • 4
    • 2342648259 scopus 로고    scopus 로고
    • Flood frequency estimation by continuous simulation of subcatchment rainfalls and discharges with the aim of improving dam safety assessment in a large basin in the czech republic
    • Blazkova, S. and K. Beven, 2004. Flood frequency estimation by continuous simulation of subcatchment rainfalls and discharges with the aim of improving dam safety assessment in a large basin in the czech republic. J. Hydrol., 292: 153-172.
    • (2004) J. Hydrol , vol.292 , pp. 153-172
    • Blazkova, S.1    Beven, K.2
  • 5
    • 33846807570 scopus 로고    scopus 로고
    • Multistep-ahead neural networks for flood forecasting
    • Chang, F.J., Y.M. Chiang and L.C. Chang, 2007. Multistep-ahead neural networks for flood forecasting. Hydrol. Sci. J., 52: 114-130.
    • (2007) Hydrol. Sci. J , vol.52 , pp. 114-130
    • Chang, F.J.1    Chiang, Y.M.2    Chang, L.C.3
  • 6
    • 27544472438 scopus 로고    scopus 로고
    • Comparison of several flood forecasting models in yangtze river
    • Chau, K.W., C.L. Wu and Y.S. Li, 2005. Comparison of several flood forecasting models in yangtze river. J. Hydrol. Eng., 10: 485-491.
    • (2005) J. Hydrol. Eng , vol.10 , pp. 485-491
    • Chau, K.W.1    Wu, C.L.2    Li, Y.S.3
  • 7
    • 33748929857 scopus 로고    scopus 로고
    • Particle swarm optimization training algorithm for ANNs in stage prediction of shing Mun river
    • Chau, K.W., 2006. Particle swarm optimization training algorithm for ANNs in stage prediction of shing Mun river. J. Hydrol., 329: 363-367.
    • (2006) J. Hydrol , vol.329 , pp. 363-367
    • Chau, K.W.1
  • 8
    • 0036845179 scopus 로고    scopus 로고
    • Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration
    • Cheng, C.T., C.P. Ou and K.W. Chau, 2002. Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration. J. Hydrol., 268: 72-86.
    • (2002) J. Hydrol , vol.268 , pp. 72-86
    • Cheng, C.T.1    Ou, C.P.2    Chau, K.W.3
  • 10
    • 0032005702 scopus 로고    scopus 로고
    • An artificial neural network approach to rainfall-runoff modeling
    • Dawson, CW. and R.L. Wilby, 1998. An artificial neural network approach to rainfall-runoff modeling. Hydrol. Sci., 43: 47-66.
    • (1998) Hydrol. Sci , vol.43 , pp. 47-66
    • Dawson, C.W.1    Wilby, R.L.2
  • 11
    • 32044443415 scopus 로고    scopus 로고
    • Flood estimation at ungauged sites using artificial neural networks
    • Dawson, C.W., R.J. Abrabart, A.Y. Shamseldin and R.L. Wilby, 2006. Flood estimation at ungauged sites using artificial neural networks. J. Hydrol., 319: 391-409.
    • (2006) J. Hydrol , vol.319 , pp. 391-409
    • Dawson, C.W.1    Abrabart, R.J.2    Shamseldin, A.Y.3    Wilby, R.L.4
  • 12
    • 23044467858 scopus 로고    scopus 로고
    • Fuzzy neural network model for hydrlogic flow routing
    • Deka, P. and V. Chandramouli, 2005. Fuzzy neural network model for hydrlogic flow routing. J. Hydrol. Eng., 10: 302-314.
    • (2005) J. Hydrol. Eng , vol.10 , pp. 302-314
    • Deka, P.1    Chandramouli, V.2
  • 14
    • 17044442585 scopus 로고    scopus 로고
    • Development of a fuzzy logic based rainfall-runoff model
    • Hundecha, Y., A. Bardossy and H.W. Theisen, 2001. Development of a fuzzy logic based rainfall-runoff model. Hydrol. Sci., 46: 363-376.
    • (2001) Hydrol. Sci , vol.46 , pp. 363-376
    • Hundecha, Y.1    Bardossy, A.2    Theisen, H.W.3
  • 15
    • 0004105094 scopus 로고    scopus 로고
    • Design of fuzzy controllers
    • Dep. Of Automation, Technical University. of Denmark, Denmark
    • Jantzen, J., 1999. Design of fuzzy controllers. Technical Rep. No. 98-E 864, Dep. Of Automation, Technical University. of Denmark, Denmark.
    • (1999) Technical Rep , Issue.98 -E , pp. 864
    • Jantzen, J.1
  • 18
    • 0033692068 scopus 로고    scopus 로고
    • River stage forecasting in bangladesh: Neural network approach
    • Liong, S.Y., W.H. Lim and G.N. Paudyal, 2000. River stage forecasting in bangladesh: Neural network approach. J. Comput. Civ. Eng., 14:1-8.
    • (2000) J. Comput. Civ. Eng , vol.14 , pp. 1-8
    • Liong, S.Y.1    Lim, W.H.2    Paudyal, G.N.3
  • 19
    • 0242306014 scopus 로고    scopus 로고
    • A real time hydrological forecasting system using a fuzzy clustering approach
    • Luchetta, A. and S. Manetti, 2003. A real time hydrological forecasting system using a fuzzy clustering approach. Comput. Geosci., 29: 1111-1117.
    • (2003) Comput. Geosci , vol.29 , pp. 1111-1117
    • Luchetta, A.1    Manetti, S.2
  • 20
    • 0346687459 scopus 로고    scopus 로고
    • Application of fuzzy logic to forecast seasonal runoff
    • Mahabir, C., F.E. Hicks and A.R. Fayek, 2003. Application of fuzzy logic to forecast seasonal runoff. Hydrol. Proc., 17: 3749-3762.
    • (2003) Hydrol. Proc , vol.17 , pp. 3749-3762
    • Mahabir, C.1    Hicks, F.E.2    Fayek, A.R.3
  • 21
    • 0033957764 scopus 로고    scopus 로고
    • Neural networks for the 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 the prediction and forecasting of water resources variables: A review of modeling issues and applications. Environ. Modeling Software, 15: 101-123.
    • (2000) Environ. Modeling Software , vol.15 , pp. 101-123
    • Maier, H.R.1    Dandy, G.C.2
  • 22
    • 0042510907 scopus 로고    scopus 로고
    • Application of artificial neural network to rapid feedback of potential ecological risk in flood diversion zone
    • Ni, J.R. and A. Xue 2003. Application of artificial neural network to rapid feedback of potential ecological risk in flood diversion zone. Eng. Applic. Artif. Intell., 16: 105-119.
    • (2003) Eng. Applic. Artif. Intell , vol.16 , pp. 105-119
    • Ni, J.R.1    Xue, A.2
  • 23
    • 0035889604 scopus 로고    scopus 로고
    • Fuzzy conceptual rainfall-runoff models
    • Ozelkan, E.C. and L. Duckstein, 2001. Fuzzy conceptual rainfall-runoff models. J. Hydrol., 253: 41-68.
    • (2001) J. Hydrol , vol.253 , pp. 41-68
    • Ozelkan, E.C.1    Duckstein, L.2
  • 24
    • 33845975322 scopus 로고    scopus 로고
    • A nonlinear perturbation model based on artificial neural network
    • Pang, B., S. Guo, L. Xiong and C. Li, 2007. A nonlinear perturbation model based on artificial neural network. J. Hydrol., 333: 504-516.
    • (2007) J. Hydrol , vol.333 , pp. 504-516
    • Pang, B.1    Guo, S.2    Xiong, L.3    Li, C.4
  • 25
    • 31044442537 scopus 로고    scopus 로고
    • Regionalization of watersheds by fuzzy cluster analysis
    • Rao, A.R. and V.V. Srinivas, 2006. Regionalization of watersheds by fuzzy cluster analysis. J. Hydrol., 318: 57-79.
    • (2006) J. Hydrol , vol.318 , pp. 57-79
    • Rao, A.R.1    Srinivas, V.V.2
  • 26
    • 0345404342 scopus 로고    scopus 로고
    • Fuzzy algorithm for estimation of solar irrigation from sunshine duration
    • Sen, Z., 1998. Fuzzy algorithm for estimation of solar irrigation from sunshine duration. Sol. Eng., 63: 39-49.
    • (1998) Sol. Eng , vol.63 , pp. 39-49
    • Sen, Z.1
  • 28
    • 33751081243 scopus 로고    scopus 로고
    • ANN and fuzzy logic models for simulating event-based rainfall-runoff
    • Tayfur, G. and V.P. Singh, 2006. ANN and fuzzy logic models for simulating event-based rainfall-runoff. J. Hydrol. Eng., 132: 1321-1330.
    • (2006) J. Hydrol. Eng , vol.132 , pp. 1321-1330
    • Tayfur, G.1    Singh, V.P.2
  • 31
    • 33845421111 scopus 로고    scopus 로고
    • Wu, C.L. and K.W. Chau, 2006. A flood forecasting neural network model with genetic algorithm. Int. J. Environ. Poll., 28.,261-273.
    • Wu, C.L. and K.W. Chau, 2006. A flood forecasting neural network model with genetic algorithm. Int. J. Environ. Poll., 28.,261-273.
  • 32
    • 0035340544 scopus 로고    scopus 로고
    • A nonlinear combination of the forecasts of rainfall-runoff models by the first-order takagi-sugeno fuzzy systems
    • Xiong, L., A.Y. Shamseldin and K.M. O'Connor, 2001. A nonlinear combination of the forecasts of rainfall-runoff models by the first-order takagi-sugeno fuzzy systems. J. Hydrol., 245: 196-217.
    • (2001) J. Hydrol , vol.245 , pp. 196-217
    • Xiong, L.1    Shamseldin, A.Y.2    O'Connor, K.M.3
  • 33
    • 34248666540 scopus 로고
    • Fuzzy sets
    • Zadeh, L.A. 1965. Fuzzy sets. Inf. Control, 8: 338-353.
    • (1965) Inf. Control , vol.8 , pp. 338-353
    • Zadeh, L.A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.