메뉴 건너뛰기




Volumn 124, Issue 5, 2015, Pages 933-943

Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment

Author keywords

Back propagation; Defuzzification; Membership function; Sigmoid; Transferred function

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; CATCHMENT; FLOW MODELING; FUZZY MATHEMATICS; MODEL VALIDATION; SEDIMENT YIELD; STREAMFLOW;

EID: 84937468013     PISSN: 02534126     EISSN: 0973774X     Source Type: Journal    
DOI: 10.1007/s12040-015-0592-7     Document Type: Article
Times cited : (17)

References (26)
  • 2
    • 15444365276 scopus 로고    scopus 로고
    • Fuzzy exemplar-based inference system for flood forecasting
    • Chang L C, Chang F J and Sai Y H T 2005 Fuzzy exemplar-based inference system for flood forecasting; Water Resour. Res. 41, doi: 10. 1029/2004WR003037.
    • (2005) Water Resour. Res , vol.41
    • Chang, L.C.1    Chang, F.J.2    Sai, Y.H.T.3
  • 3
    • 0032123339 scopus 로고    scopus 로고
    • Runoff forecasting using RBF networks with OLS algorithm
    • Fernando D A K and Jayawardena A W 1998 Runoff forecasting using RBF networks with OLS algorithm; J. Hydrol. Eng. 3 203-209.
    • (1998) J. Hydrol. Eng , vol.3 , pp. 203-209
    • Fernando, D.A.K.1    Jayawardena, A.W.2
  • 4
    • 0033197895 scopus 로고    scopus 로고
    • Application of ANN for reservoir in flow prediction and operation
    • Jain S K and Srivastava D K 1999 Application of ANN for reservoir in flow prediction and operation; J. Water Resour. Plan. Manag. 125 263-271.
    • (1999) J. Water Resour. Plan. Manag , vol.125 , pp. 263-271
    • Jain, S.K.1    Srivastava, D.K.2
  • 5
    • 85039882860 scopus 로고    scopus 로고
    • Runoff modelling using different membership functions in adaptive neuro fuzzy inference system; Int
    • Gowda C C and Moyya S D 2014 Runoff modelling using different membership functions in adaptive neuro fuzzy inference system; Int. J. Adv. Eng. Sci. 4(4) 48-51.
    • (2014) J. Adv. Eng. Sci , vol.4 , Issue.4 , pp. 48-51
    • Gowda, C.C.1    Moyya, S.D.2
  • 6
    • 0036715542 scopus 로고    scopus 로고
    • Dynamic fuzzy modelling of storm water infiltration in urban fractured aquifers; J
    • Hong Y S, Rosen M R and Reeves R R 2002 Dynamic fuzzy modelling of storm water infiltration in urban fractured aquifers; J. Hydrol. Eng. 7(5) 380-391.
    • (2002) Hydrol. Eng , vol.7 , Issue.5 , pp. 380-391
    • Hong, Y.S.1    Rosen, M.R.2    Reeves, R.R.3
  • 7
    • 0029413797 scopus 로고
    • Artificial neural network modelling of the rainfall-runoff process
    • Hsu K, Gupta H V and Sorooshian S 1995 Artificial neural network modelling of the rainfall-runoff process; Water Resour. Res. 31 2517-2530.
    • (1995) Water Resour. Res , vol.31 , pp. 2517-2530
    • Hsu, K.1    Gupta, H.V.2    Sorooshian, S.3
  • 8
    • 0016451032 scopus 로고
    • An experiment in linguistic synthesis with a fuzzy logic controller; Int
    • Mamdani E H and Assilian S 1975 An experiment in linguistic synthesis with a fuzzy logic controller; Int. J. Man-Machine Studies 7(1) 1-13.
    • (1975) J. Man-Machine Studies , vol.7 , Issue.1 , pp. 1-13
    • Mamdani, E.H.1    Assilian, S.2
  • 9
    • 7244247371 scopus 로고    scopus 로고
    • Treatment of precipitation uncertainty in rainfall-runoff modelling: A fuzzy set approach; Adv
    • Maskey S, Guinot V and Price R K 2004 Treatment of precipitation uncertainty in rainfall-runoff modelling: A fuzzy set approach; Adv. Water Resour. 27(9) 889-898
    • (2004) Water Resour , vol.27 , Issue.9 , pp. 889-898
    • Maskey, S.1    Guinot, V.2    Price, R.K.3
  • 10
    • 66249107437 scopus 로고    scopus 로고
    • Flood forecasting using ANN, Neuro-Fuzzy, and Neuro-GA models; J
    • Mukerji A, Chatterjee Chandranath and Raghuwanshi Narendra Singh 2009 Flood forecasting using ANN, Neuro-Fuzzy, and Neuro-GA models; J. Hydrol. Eng. 14(6) 647-652.
    • (2009) Hydrol. Eng , vol.14 , Issue.6 , pp. 647-652
    • Singh, N.1
  • 11
    • 0001660293 scopus 로고
    • River flow forecasting through conceptual models
    • Nash J F and Sutcliffe J V 1970 River flow forecasting through conceptual models; J. Hydrol. Sci. 44 399-417.
    • (1970) J. Hydrol. Sci , vol.44 , pp. 399-417
    • Nash, J.F.1    Sutcliffe, J.V.2
  • 12
    • 14844352523 scopus 로고    scopus 로고
    • Fuzzy computing based rainfall-runoff model for real time flood forecasting
    • Nayak P C, Sudheer K P and Ramasastri K S 2005 Fuzzy computing based rainfall-runoff model for real time flood forecasting; J. Hydrol. Process. 9 955-968.
    • (2005) J. Hydrol. Process , vol.9 , pp. 955-968
    • Nayak, P.C.1    Sudheer, K.P.2    Ramasastri, K.S.3
  • 13
    • 31444443313 scopus 로고    scopus 로고
    • Runoff and sediment yield modelling using artificial neural networks: Upper Siwane River, India
    • Raghuwanshi N S, Singh R and Reddy L S 2006 Runoff and sediment yield modelling using artificial neural networks: Upper Siwane River, India; J. Hydrol. Eng. 11 71-79.
    • (2006) J. Hydrol. Eng , vol.11 , pp. 71-79
    • Raghuwanshi, N.S.1    Singh, R.2    Reddy, L.S.3
  • 15
    • 34249898039 scopus 로고    scopus 로고
    • A simple neural network model for the determination of aquifer parameters
    • Samani N, Gohari-Moghadam M and Safavi A A 2007 A simple neural network model for the determination of aquifer parameters; J. Hydrol. 340 1-11, doi: 0. 1016/j. jhydrol. 2007. 03. 017.
    • (2007) J. Hydrol , vol.340 , pp. 1-11
    • Samani, N.1    Gohari-Moghadam, M.2    Safavi, A.A.3
  • 16
    • 84859363621 scopus 로고    scopus 로고
    • Modelling of suspended sediment concentration at Kasol in India using ANN, fuzzy logic, and decision tree algorithms; J
    • Senthil Kumar A R, Ojha C S P, Goyal Manish Kumar, Singh R D and Swamee P K 2012 Modelling of suspended sediment concentration at Kasol in India using ANN, fuzzy logic, and decision tree algorithms; J. Hydrol. Eng. ASAE 17(3) 394-404.
    • (2012) Hydrol. Eng. ASAE , vol.17 , Issue.3 , pp. 394-404
    • Senthil Kumar, A.R.1    Ojha, C.S.P.2    Kumar, G.M.3    Singh, R.D.4    Swamee, P.K.5
  • 17
    • 85039891306 scopus 로고    scopus 로고
    • Class notes, Civil Engineering Faculty, Istanbul Technical Univ., Istanbul, Turkey (in Turkish)
    • Sen Z 1999 Fuzzy modelling in engineering; Class notes, Civil Engineering Faculty, Istanbul Technical Univ., Istanbul, Turkey (in Turkish).
    • (1999) Fuzzy modelling in engineering
    • Sen, Z.1
  • 18
    • 85039873783 scopus 로고    scopus 로고
    • Rainfall runoff modelling using multi-layer perceptron technique-A case study of the Upper Kharun Catchments in Chhattishgarh; J
    • Sinha Jitendra R K, Sahu Agrawal Avinash, Pali A K and Sinha B L 2013 Rainfall runoff modelling using multi-layer perceptron technique-A case study of the Upper Kharun Catchments in Chhattishgarh; J. Agr. Eng. 50(2) 43-51.
    • (2013) Agr. Eng , vol.50 , Issue.2 , pp. 43-51
    • Sinha Jitendra, R.K.1    Avinash, S.A.2    Pali, A.K.3    Sinha, B.L.4
  • 19
    • 0037199712 scopus 로고    scopus 로고
    • River flow forecasting: Use of phase space reconstruction and artificial neural network approaches
    • Shivakumar B, Jayawardhane A W and Fernando T M K G 2002 River flow forecasting: Use of phase space reconstruction and artificial neural network approaches; J. Hydrol. 265 225-245.
    • (2002) J. Hydrol , vol.265 , pp. 225-245
    • Shivakumar, B.1    Jayawardhane, A.W.2    Fernando, T.M.K.G.3
  • 20
    • 0029416249 scopus 로고
    • Neural network model for rainfall runoff process
    • Smith J and Sli R N 1995 Neural network model for rainfall runoff process; J. Water Res. Plan. Manag. ASCE 121 49-508.
    • (1995) J. Water Res. Plan. Manag. ASCE , vol.121 , pp. 49-508
    • Smith, J.1    Sli, R.N.2
  • 21
    • 33751081243 scopus 로고    scopus 로고
    • ANN and fuzzy logic models for simulating event-based rainfall-runoff
    • Tayfur G and Singh V P 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
  • 22
    • 34447334519 scopus 로고    scopus 로고
    • Predicting and forecasting flow discharge at sites receiving significant lateral inflow
    • Tayfur G, Moramorco T and Singh V P 2003 Predicting and forecasting flow discharge at sites receiving significant lateral inflow; Hydrol. Process. 21 1848-1859.
    • (2003) Hydrol. Process , vol.21 , pp. 1848-1859
    • Tayfur, G.1    Moramorco, T.2    Singh, V.P.3
  • 23
    • 0033167344 scopus 로고    scopus 로고
    • Rainfall-runoff modelling using artificial neural networks
    • Tokar A S and Johnson P A 1999 Rainfall-runoff modelling using artificial neural networks; J. Hydrol. Eng. 4 232-239.
    • (1999) J. Hydrol. Eng , vol.4 , pp. 232-239
    • Tokar, A.S.1    Johnson, P.A.2
  • 24
    • 0036844199 scopus 로고    scopus 로고
    • Comparison of fuzzy and nonfuzzy optimal reservoir operating policies; J
    • ()
    • Tilmant A, Vanclooster M, Duckstein L and Persoons E 2002 Comparison of fuzzy and nonfuzzy optimal reservoir operating policies; J. Water Resour. Plan. Manag. 128(6) 390-398.
    • (2002) Water Resour. Plan. Manag , vol.128 , Issue.6 , pp. 390-398
    • Tilmant, A.1    Vanclooster, M.2    Duckstein, L.3    Persoons, E.4
  • 25
    • 17044442585 scopus 로고    scopus 로고
    • Development of fuzzy logic based rainfall runoff model; J
    • Yeshewatesfa Hundecha, Andrass Bardossey and Hanse Warner Theisen 2001 Development of fuzzy logic based rainfall runoff model; J. Hydrol. Sci. 46(3) 363-376.
    • (2001) Hydrol. Sci , vol.46 , Issue.3 , pp. 363-376
    • Hundecha, Y.1    Bardossey, A.2    Theisen, H.W.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.