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Volumn 24, Issue 3-4, 2014, Pages 695-703

Multilayer perceptron with different training algorithms for streamflow forecasting

Author keywords

Algorithms; Forecasting; Multilayer perceptron; Streamflow

Indexed keywords


EID: 84893923942     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-1287-5     Document Type: Article
Times cited : (32)

References (52)
  • 1
    • 84884816020 scopus 로고    scopus 로고
    • Artificial neural network-genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran
    • doi:10.1007/s00521-012-1087-y
    • Aghajanloo MB, Sabziparvar AA, Hosseinzadeh Talaee P (2012) Artificial neural network-genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran. Neural Comput Appl. doi: 10. 1007/s00521-012-1087-y.
    • (2012) Neural Comput Appl
    • Aghajanloo, M.B.1    Sabziparvar, A.A.2    Hosseinzadeh Talaee, P.3
  • 2
    • 34248202148 scopus 로고    scopus 로고
    • Artificial neural network model for synthetic streamflow generation
    • Ahmed JA, Sarma AK (2009) Artificial neural network model for synthetic streamflow generation. Water Resour Manage 21: 1015-1029.
    • (2009) Water Resour Manage , vol.21 , pp. 1015-1029
    • Ahmed, J.A.1    Sarma, A.K.2
  • 3
    • 84867896968 scopus 로고    scopus 로고
    • Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of Çoruh basin, Turkey
    • doi:10.1111/j.1747-6593.2012.00337.x
    • Can I, Tosunoǧlu F, Kahya E (2012) Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of Çoruh basin, Turkey. Hydrol Process doi. doi: 10. 1111/j. 1747-6593. 2012. 00337. x.
    • (2012) Hydrol Process Doi
    • Can, I.1    Tosunoǧlu, F.2    Kahya, E.3
  • 4
    • 0032829433 scopus 로고    scopus 로고
    • Pre'vision hydrologique par re'seaux de neurones artificiels: e'tat de l'art
    • Coulibaly P, Anctil F, Bobe'e B (1999) Pre'vision hydrologique par re'seaux de neurones artificiels: e'tat de l'art. Can J Civil Eng 26: 293-304.
    • (1999) Can J Civil Eng , vol.26 , pp. 293-304
    • Coulibaly, P.1    Anctil, F.2    Bobe'e, B.3
  • 5
    • 0035450182 scopus 로고    scopus 로고
    • Multivariate reservoir inflow forecasting using temporal neural network
    • Coulibaly P, Anctil F, Bobe'e B (2001) Multivariate reservoir inflow forecasting using temporal neural network. J Hydrol Eng ASCE 6(5): 367-376.
    • (2001) J Hydrol Eng ASCE , vol.6 , Issue.5 , pp. 367-376
    • Coulibaly, P.1    Anctil, F.2    Bobe'e, B.3
  • 6
    • 0024861871 scopus 로고
    • Approximation by superposition of a sigmoidal function
    • Cybenko G (1989) Approximation by superposition of a sigmoidal function. Math Control Signals Syst 2: 303-314.
    • (1989) Math Control Signals Syst , vol.2 , pp. 303-314
    • Cybenko, G.1
  • 7
    • 0034136344 scopus 로고    scopus 로고
    • Inductive learning approaches to rainfall-runoff modelling
    • Dawson CW, Brown MA, Wilby R (2000) Inductive learning approaches to rainfall-runoff modelling. Int J Neural Sys 10: 43-57.
    • (2000) Int J Neural Sys , vol.10 , pp. 43-57
    • Dawson, C.W.1    Brown, M.A.2    Wilby, R.3
  • 8
    • 0036698154 scopus 로고    scopus 로고
    • Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China
    • Dawson CW, Harpham C, Wilby RL, Chen Y (2002) Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China. Hydrol Earth Syst Sci 6(4): 619-626.
    • (2002) Hydrol Earth Syst Sci , vol.6 , Issue.4 , pp. 619-626
    • Dawson, C.W.1    Harpham, C.2    Wilby, R.L.3    Chen, Y.4
  • 9
    • 0032005702 scopus 로고    scopus 로고
    • An artificial neural network approach to rainfall-runoff modelling
    • Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall-runoff modelling. Hydrol Sci J 43: 47-66.
    • (1998) Hydrol Sci J , vol.43 , pp. 47-66
    • Dawson, C.W.1    Wilby, R.2
  • 10
    • 10644295753 scopus 로고    scopus 로고
    • Input determination for neural network models in water resources applications, Part1-background and methodology
    • Gavin B, Graeme D, Holger M (2005) Input determination for neural network models in water resources applications, Part1-background and methodology. J Hydrol 301(1): 75-92.
    • (2005) J Hydrol , vol.301 , Issue.1 , pp. 75-92
    • Gavin, B.1    Graeme, D.2    Holger, M.3
  • 11
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2: 359-366.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 12
    • 84860213476 scopus 로고    scopus 로고
    • Numerical model and computational intelligence approaches for estimating flow through rockfill dam
    • Hosseinzadeh Talaee P, Heydari M, Fathi P, Marofi S, Tabari H (2012) Numerical model and computational intelligence approaches for estimating flow through rockfill dam. J Hydrol Eng ASCE 17(14): 528-536.
    • (2012) J Hydrol Eng ASCE , vol.17 , Issue.14 , pp. 528-536
    • Hosseinzadeh Talaee, P.1    Heydari, M.2    Fathi, P.3    Marofi, S.4    Tabari, H.5
  • 13
    • 0029413797 scopus 로고
    • Artificial neural network modeling of rainfall-runoff process
    • Hsu KL, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of rainfall-runoff process. Water Resour Res 31(10): 2517-2530.
    • (1995) Water Resour Res , vol.31 , Issue.10 , pp. 2517-2530
    • Hsu, K.L.1    Gupta, H.V.2    Sorooshian, S.3
  • 14
    • 46449089657 scopus 로고    scopus 로고
    • Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation
    • Jain SK, Nayak PC, Sudheer KP (2008) Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrol Process 22: 2225-2234.
    • (2008) Hydrol Process , vol.22 , pp. 2225-2234
    • Jain, S.K.1    Nayak, P.C.2    Sudheer, K.P.3
  • 15
    • 84881328588 scopus 로고    scopus 로고
    • Improving streamflow forecast lead time using oceanic-atmospheric oscillations for Kaidu River Basin, Xinjiang, China
    • doi:10.1061/(ASCE)HE.1943-5584.0000707
    • Kalra A, Li L, Li X, Ahmad S (2012) Improving streamflow forecast lead time using oceanic-atmospheric oscillations for Kaidu River Basin, Xinjiang, China. J Hydrol Eng doi: 10. 1061/(ASCE)HE. 1943-5584. 0000707.
    • (2012) J Hydrol Eng Doi
    • Kalra, A.1    Li, L.2    Li, X.3    Ahmad, S.4
  • 17
    • 1642497522 scopus 로고    scopus 로고
    • River flow modeling using Artificial Neural Networks
    • Kisi O (2004) River flow modeling using Artificial Neural Networks. J Hydrol Eng ASCE 9(1): 60-63.
    • (2004) J Hydrol Eng ASCE , vol.9 , Issue.1 , pp. 60-63
    • Kisi, O.1
  • 18
    • 12544259920 scopus 로고    scopus 로고
    • Daily river flow forecasting using artificial neural networks and auto-regressive models
    • Kisi O (2005) Daily river flow forecasting using artificial neural networks and auto-regressive models. Turk J Eng Env Sci 29: 9-20.
    • (2005) Turk J Eng Env Sci , vol.29 , pp. 9-20
    • Kisi, O.1
  • 19
    • 34548146808 scopus 로고    scopus 로고
    • Streamflow forecasting using different artificial neural network algorithms
    • Kisi O (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng ASCE 12(5): 532-539.
    • (2007) J Hydrol Eng ASCE , vol.12 , Issue.5 , pp. 532-539
    • Kisi, O.1
  • 20
    • 41949142664 scopus 로고    scopus 로고
    • River flow forecasting and estimation using different artificial neural network techniques
    • Kisi O (2008) River flow forecasting and estimation using different artificial neural network techniques. Hydrol Res 39(1): 27-40.
    • (2008) Hydrol Res , vol.39 , Issue.1 , pp. 27-40
    • Kisi, O.1
  • 22
    • 16444365723 scopus 로고    scopus 로고
    • Rainfall-runoff modeling using artificial neural networks: comparison of network types
    • Kumar APS, Sudheer KP, Jain SK, Agarwal PK (2005) Rainfall-runoff modeling using artificial neural networks: comparison of network types. Hydrol Process 19: 1277-1291.
    • (2005) Hydrol Process , vol.19 , pp. 1277-1291
    • Kumar, A.P.S.1    Sudheer, K.P.2    Jain, S.K.3    Agarwal, P.K.4
  • 23
    • 0033957764 scopus 로고    scopus 로고
    • Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications
    • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Soft 15: 101-124.
    • (2000) Environ Model Soft , vol.15 , pp. 101-124
    • Maier, H.R.1    Dandy, G.C.2
  • 24
    • 79952738488 scopus 로고    scopus 로고
    • Predicting spatial distribution of snow water equivalent using multivariate non-linear regression and computational intelligence methods
    • Marofi S, Tabari H, Zare Abyaneh H (2011) Predicting spatial distribution of snow water equivalent using multivariate non-linear regression and computational intelligence methods. Water Resour Manage 25: 1417-1435.
    • (2011) Water Resour Manage , vol.25 , pp. 1417-1435
    • Marofi, S.1    Tabari, H.2    Zare Abyaneh, H.3
  • 26
    • 63149104793 scopus 로고    scopus 로고
    • Multi-criteria validation of artificial neural network rainfall-runoff modeling
    • Modarres R (2009) Multi-criteria validation of artificial neural network rainfall-runoff modeling. Hydrol Earth Syst Sci 13: 411-421.
    • (2009) Hydrol Earth Syst Sci , vol.13 , pp. 411-421
    • Modarres, R.1
  • 27
    • 3142538909 scopus 로고    scopus 로고
    • Improved streamflow forecasting using self-organizing radial basis function artificial neural networks
    • Moradkhani H, Hsu K-L, Gupta HV, Sorooshian S (2004) Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. J Hydrol 295(1-4): 246-262.
    • (2004) J Hydrol , vol.295 , Issue.1-4 , pp. 246-262
    • Moradkhani, H.1    Hsu, K.-L.2    Gupta, H.V.3    Sorooshian, S.4
  • 28
    • 0001362410 scopus 로고
    • The Levenberg-Marquardt algorithm: implementation and theory, numerical analysis
    • G. A. Watson (Ed.), New York: Springer
    • More JJ (1977) The Levenberg-Marquardt algorithm: implementation and theory, numerical analysis. In: Watson GA (ed) Lecture notes in mathematics 630. Springer, New York, pp 105-116.
    • (1977) Lecture Notes in Mathematics 630 , pp. 105-116
    • More, J.J.1
  • 29
    • 19044383810 scopus 로고    scopus 로고
    • Short-term flood forecasting with a neurofuzzy model
    • doi:10.1029/2004WR003562
    • Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2005) Short-term flood forecasting with a neurofuzzy model. Water Resour Res 41: W04004. doi: 10. 1029/2004WR003562.
    • (2005) Water Resour Res , vol.41
    • Nayak, P.C.1    Sudheer, K.P.2    Rangan, D.M.3    Ramasastri, K.S.4
  • 31
    • 77955735474 scopus 로고    scopus 로고
    • Daily outflow prediction by multilayer perceptron with logistic sigmoid and tangent sigmoid activation functions
    • Rezaeian Zadeh M, Amin S, Khalili D, Singh VP (2010) Daily outflow prediction by multilayer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manage 24(11): 2673-2688.
    • (2010) Water Resour Manage , vol.24 , Issue.11 , pp. 2673-2688
    • Rezaeian Zadeh, M.1    Amin, S.2    Khalili, D.3    Singh, V.P.4
  • 32
    • 84879272559 scopus 로고    scopus 로고
    • Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions
    • doi:10.1007/s12517-011-0517-y
    • Rezaeian-Zadeh M, Tabari H, Abghari H (2012) Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions. Arab J Geosci. doi: 10. 1007/s12517-011-0517-y.
    • (2012) Arab J Geosci
    • Rezaeian-Zadeh, M.1    Tabari, H.2    Abghari, H.3
  • 34
    • 84908614016 scopus 로고    scopus 로고
    • State of the art of artificial neural networks in geotechnical engineering
    • Shahin MA, Jaksa MB, Maier HR (2008) State of the art of artificial neural networks in geotechnical engineering. Electron. J Geotech Engin 8: 1-26.
    • (2008) Electron. J Geotech Engin , vol.8 , pp. 1-26
    • Shahin, M.A.1    Jaksa, M.B.2    Maier, H.R.3
  • 35
    • 0342506462 scopus 로고    scopus 로고
    • Application of neural network technique to rainfall-runoff modeling
    • Shamseldin AY (1997) Application of neural network technique to rainfall-runoff modeling. J Hydrol 199(3-4): 272-294.
    • (1997) J Hydrol , vol.199 , Issue.3-4 , pp. 272-294
    • Shamseldin, A.Y.1
  • 36
    • 0037199712 scopus 로고    scopus 로고
    • River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches
    • Sivakumar B, Jayawardena AW, Fernando TMKG (2002) River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. J Hydrol 265: 225-245.
    • (2002) J Hydrol , vol.265 , pp. 225-245
    • Sivakumar, B.1    Jayawardena, A.W.2    Fernando, T.M.K.G.3
  • 39
    • 0037197571 scopus 로고    scopus 로고
    • A data driven algorithm for constructing artificial neural network rainfall-runoff models
    • Sudheer KP, Gosain AK, Ramasastri KS (2002) A data driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 16(6): 1325-1330.
    • (2002) Hydrol Process , vol.16 , Issue.6 , pp. 1325-1330
    • Sudheer, K.P.1    Gosain, A.K.2    Ramasastri, K.S.3
  • 40
    • 1642333234 scopus 로고    scopus 로고
    • Explaining the internal behaviour of artificial neural network river flow models
    • Sudheer KP, Jain A (2004) Explaining the internal behaviour of artificial neural network river flow models. Hydrol Process 18(4): 833-844.
    • (2004) Hydrol Process , vol.18 , Issue.4 , pp. 833-844
    • Sudheer, K.P.1    Jain, A.2
  • 41
    • 84880729160 scopus 로고    scopus 로고
    • Multilayer perceptron for reference evapotranspiration estimation in a semiarid region
    • doi:10.1007/s00521-012-0904-7
    • Tabari H, Hosseinzadeh Talaee P (2012) Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Comput Appl. doi: 10. 1007/s00521-012-0904-7.
    • (2012) Neural Comput Appl
    • Tabari, H.1    Hosseinzadeh Talaee, P.2
  • 42
    • 77953692036 scopus 로고    scopus 로고
    • Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression
    • Tabari H, Marofi S, Sabziparvar AA (2010) Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrig Sci 28: 399-406.
    • (2010) Irrig Sci , vol.28 , pp. 399-406
    • Tabari, H.1    Marofi, S.2    Sabziparvar, A.A.3
  • 43
    • 77952883472 scopus 로고    scopus 로고
    • Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran
    • Tabari H, Marofi S, Zare Abyaneh H, Sharifi MR (2010) Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran. Neural Comput Applic 19: 625-635.
    • (2010) Neural Comput Applic , vol.19 , pp. 625-635
    • Tabari, H.1    Marofi, S.2    Zare Abyaneh, H.3    Sharifi, M.R.4
  • 44
    • 78650738248 scopus 로고    scopus 로고
    • Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region
    • Tabari H, Sabziparvar AA, Ahmadi M (2011) Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteor Atmos Phys 110: 135-142.
    • (2011) Meteor Atmos Phys , vol.110 , pp. 135-142
    • Tabari, H.1    Sabziparvar, A.A.2    Ahmadi, M.3
  • 45
    • 0033167344 scopus 로고    scopus 로고
    • Rainfall-runoff modeling using artificial neural networks
    • Tokar AS, Johnson A (1999) Rainfall-runoff modeling using artificial neural networks. J Hydrol Eng ASCE 4(3): 232-239.
    • (1999) J Hydrol Eng ASCE , vol.4 , Issue.3 , pp. 232-239
    • Tokar, A.S.1    Johnson, A.2
  • 46
    • 0034174397 scopus 로고    scopus 로고
    • Precipitation-runoff modeling using artificial neural networks and conceptual models
    • Tokar AS, Markus M (2000) Precipitation-runoff modeling using artificial neural networks and conceptual models. J. Hydrol. Eng. ASCE 5(2): 156-161.
    • (2000) J. Hydrol. Eng. ASCE , vol.5 , Issue.2 , pp. 156-161
    • Tokar, A.S.1    Markus, M.2
  • 48
    • 33646547633 scopus 로고    scopus 로고
    • Forecasting daily streamflow using hybrid ANN models
    • Wang W, van Gelder PHAJM, Vrijling JK, Ma J (2006) Forecasting daily streamflow using hybrid ANN models. J Hydrol 324(1-2): 383-399.
    • (2006) J Hydrol , vol.324 , Issue.1-2 , pp. 383-399
    • Wang, W.1    van Gelder, P.H.A.J.M.2    Vrijling, J.K.3    Ma, J.4
  • 49
    • 18744366631 scopus 로고    scopus 로고
    • Artificial Neural Networks for Forecasting Watershed Runoff and Stream Flows
    • Wu JS, Han J, Annambhotla S, Bryant S (2005) Artificial Neural Networks for Forecasting Watershed Runoff and Stream Flows. J Hydrol Eng ASCE 10(3): 216-222.
    • (2005) J Hydrol Eng ASCE , vol.10 , Issue.3 , pp. 216-222
    • Wu, J.S.1    Han, J.2    Annambhotla, S.3    Bryant, S.4
  • 50
    • 84866064802 scopus 로고    scopus 로고
    • Stream flow forecasting using Levenberg-Marquardt algorithm approach
    • Yadav D, Naresh R, Sharma V (2011) Stream flow forecasting using Levenberg-Marquardt algorithm approach. Int J Water Resour Environ Eng 3(1): 30-40.
    • (2011) Int J Water Resour Environ Eng , vol.3 , Issue.1 , pp. 30-40
    • Yadav, D.1    Naresh, R.2    Sharma, V.3
  • 51
    • 77953342831 scopus 로고    scopus 로고
    • Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting
    • Yonaba H, Anctil F, Fortin V (2010) Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting. J Hydrol Eng ASCE 15(4): 275-283.
    • (2010) J Hydrol Eng ASCE , vol.15 , Issue.4 , pp. 275-283
    • Yonaba, H.1    Anctil, F.2    Fortin, V.3
  • 52
    • 0003123930 scopus 로고    scopus 로고
    • Forecasting with artificial neural networks: the state of the art
    • Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecasting 14: 35-62.
    • (1998) Int J Forecasting , vol.14 , pp. 35-62
    • Zhang, G.1    Patuwo, B.E.2    Hu, M.Y.3


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