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Volumn 365, Issue 3-4, 2009, Pages 329-334

Modelling level change in lakes using neuro-fuzzy and artificial neural networks

Author keywords

Artificial neural networks; Lake Beysehir; Level change; Neuro fuzzy

Indexed keywords

BACKPROPAGATION; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; LAKES; MEAN SQUARE ERROR; STRAIN ENERGY; WATER CONSERVATION; WATER LEVELS;

EID: 58849094959     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2008.12.006     Document Type: Article
Times cited : (61)

References (23)
  • 1
    • 0344394527 scopus 로고    scopus 로고
    • River flow forecast for reservoir management through neural networks
    • Baratti R., Cannas B., Fanni A., Pintus M., Sechi G.M., and Toreno N. River flow forecast for reservoir management through neural networks. Neurocomputing 55 3-4 (2003) 421-437
    • (2003) Neurocomputing , vol.55 , Issue.3-4 , pp. 421-437
    • Baratti, R.1    Cannas, B.2    Fanni, A.3    Pintus, M.4    Sechi, G.M.5    Toreno, N.6
  • 2
    • 0035340711 scopus 로고    scopus 로고
    • A counterpropagation fuzzy-neural network modeling approach to real time stream flow prediction
    • Chang F.J., and Chen Y.C. A counterpropagation fuzzy-neural network modeling approach to real time stream flow prediction. J. Hydrol. 245 (2001) 153-164
    • (2001) J. Hydrol. , vol.245 , pp. 153-164
    • Chang, F.J.1    Chen, Y.C.2
  • 3
    • 0842349306 scopus 로고    scopus 로고
    • A two-step-ahead recurrent neural network for stream-flow forecasting
    • Chang L.C., Chang F.J., and Chiang Y.M. A two-step-ahead recurrent neural network for stream-flow forecasting. Hydrol. Process. 18 1 (2004) 81-92
    • (2004) Hydrol. Process. , vol.18 , Issue.1 , pp. 81-92
    • Chang, L.C.1    Chang, F.J.2    Chiang, Y.M.3
  • 4
    • 0032005702 scopus 로고    scopus 로고
    • An artificial neural network approach to rainfall-runoff modeling
    • Dawson C.W., and Wilby R.L. An artificial neural network approach to rainfall-runoff modeling. Hydrol. Sci. 43 1 (1998) 47-67
    • (1998) Hydrol. Sci. , vol.43 , Issue.1 , pp. 47-67
    • Dawson, C.W.1    Wilby, R.L.2
  • 5
    • 58849155846 scopus 로고    scopus 로고
    • Drake, J.T., 2000. Communications phase synchronization using the adaptive network fuzzy inference system. Ph.D. Thesis, New Mexico State University, Las Cruces, New Mexico, USA.
    • Drake, J.T., 2000. Communications phase synchronization using the adaptive network fuzzy inference system. Ph.D. Thesis, New Mexico State University, Las Cruces, New Mexico, USA.
  • 6
    • 0027007868 scopus 로고
    • Rainfall forecasting in space and time using a neural network
    • French M.N., Krajewski W.F., and Cuykendall R.R. Rainfall forecasting in space and time using a neural network. J. Hydrol. 137 (1992) 1-31
    • (1992) J. Hydrol. , vol.137 , pp. 1-31
    • French, M.N.1    Krajewski, W.F.2    Cuykendall, R.R.3
  • 7
    • 0842334546 scopus 로고    scopus 로고
    • A neural network approach to real-time rainfall estimation for Africa using satellite data
    • Grimes D.I.F., Coppola E., Verdecchia M., and Visconti G. A neural network approach to real-time rainfall estimation for Africa using satellite data. J. Hydromet. 4 (2003) 1119-1133
    • (2003) J. Hydromet. , vol.4 , pp. 1119-1133
    • Grimes, D.I.F.1    Coppola, E.2    Verdecchia, M.3    Visconti, G.4
  • 9
    • 0036579528 scopus 로고    scopus 로고
    • Reservoir operation using the neural network and fuzzy systems for dam control and operation support
    • Hasebe M., and Nagayama Y. Reservoir operation using the neural network and fuzzy systems for dam control and operation support. Adv. Eng. Software 33 5 (2002) 245-260
    • (2002) Adv. Eng. Software , vol.33 , Issue.5 , pp. 245-260
    • Hasebe, M.1    Nagayama, Y.2
  • 11
    • 0029413797 scopus 로고
    • Artificial neural network modeling of the rainfall-runoff process
    • Hsu K.L., Gupta H.V., and Sorooshian S. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res. 31 10 (1995) 2517-2530
    • (1995) Water Resour. Res. , vol.31 , Issue.10 , pp. 2517-2530
    • Hsu, K.L.1    Gupta, H.V.2    Sorooshian, S.3
  • 12
    • 0033197895 scopus 로고    scopus 로고
    • Application of ANN for reservoir inflow prediction and operation
    • Jain S.K., Das A., and Srivastava D.K. Application of ANN for reservoir inflow prediction and operation. J. Water Resour. Plan Manage. 125 5 (1999) 263-271
    • (1999) J. Water Resour. Plan Manage. , vol.125 , Issue.5 , pp. 263-271
    • Jain, S.K.1    Das, A.2    Srivastava, D.K.3
  • 13
    • 0027601884 scopus 로고
    • ANFIS: adaptive-network-based fuzzy inference system
    • Jang J.-S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Manag. Cyber 23 3 (1993) 665-685
    • (1993) IEEE Trans. Syst. Manag. Cyber , vol.23 , Issue.3 , pp. 665-685
    • Jang, J.-S.R.1
  • 16
    • 33748933705 scopus 로고    scopus 로고
    • Daily pan evaporation modelling using a neuro-fuzzy computing technique
    • Kisi O. Daily pan evaporation modelling using a neuro-fuzzy computing technique. J. Hydrol. 329 (2006) 636-646
    • (2006) J. Hydrol. , vol.329 , pp. 636-646
    • Kisi, O.1
  • 17
    • 0037868266 scopus 로고    scopus 로고
    • Evaluation and forecasting of daily groundwater outflow in a small chalky watershed
    • Lallahem S., and Mania J. Evaluation and forecasting of daily groundwater outflow in a small chalky watershed. Hydrol. Process. 17 8 (2003) 1561-1577
    • (2003) Hydrol. Process. , vol.17 , Issue.8 , pp. 1561-1577
    • Lallahem, S.1    Mania, J.2
  • 18
    • 0034737033 scopus 로고    scopus 로고
    • A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting
    • Luk K.C., Ball JE., and Sharma A. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J. Hydrol. 227 (2000) 56-65
    • (2000) J. Hydrol. , vol.227 , pp. 56-65
    • Luk, K.C.1    Ball, JE.2    Sharma, A.3
  • 19
    • 0030224015 scopus 로고    scopus 로고
    • Deriving a general operating policy for reservoirs using neural network
    • Raman H., and Chandramouli V. Deriving a general operating policy for reservoirs using neural network. J. Water Resour. Plan Manage. 122 5 (1996) 342-347
    • (1996) J. Water Resour. Plan Manage. , vol.122 , Issue.5 , pp. 342-347
    • Raman, H.1    Chandramouli, V.2
  • 20
    • 0003444646 scopus 로고
    • Rumelhart D.E., Hinton G.E., and Williams R.J. (Eds). Rumelhart D.E., and McClelland J.L. (Eds), MIT Press, Cambridge, MA
    • In: Rumelhart D.E., Hinton G.E., and Williams R.J. (Eds). Learning internal representation by error propagation. In: Rumelhart D.E., and McClelland J.L. (Eds). Parallel Distributed Processing. Foundations vol. 1 (1986), MIT Press, Cambridge, MA
    • (1986) Parallel Distributed Processing. Foundations , vol.1
  • 21
    • 0037200134 scopus 로고    scopus 로고
    • Short-term inflow forecasting using an artificial neural network model
    • Xu Z.X., and Li J.Y. Short-term inflow forecasting using an artificial neural network model. Hydrol. Process. 16 (2002) 2433-2439
    • (2002) Hydrol. Process. , vol.16 , pp. 2433-2439
    • Xu, Z.X.1    Li, J.Y.2
  • 23
    • 0000251270 scopus 로고
    • Comparisons between fuzzy reasoning and neural network methods to forecast runoff discharge
    • Zhu M.L., and Fujita M. Comparisons between fuzzy reasoning and neural network methods to forecast runoff discharge. J. Hydrosci. Hydraul. Eng. 12 2 (1994) 131-141
    • (1994) J. Hydrosci. Hydraul. Eng. , vol.12 , Issue.2 , pp. 131-141
    • Zhu, M.L.1    Fujita, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.