메뉴 건너뛰기




Volumn 27, Issue 6, 2016, Pages 1533-1542

RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia

Author keywords

Artificial neural networks; FFNN; RBFNN; Streamflow forecasting

Indexed keywords

BACKPROPAGATION; COMPLEX NETWORKS; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; RIVERS; STREAM FLOW; WATER RESOURCES;

EID: 84933545314     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-015-1952-6     Document Type: Article
Times cited : (98)

References (29)
  • 1
    • 0004311217 scopus 로고
    • Time series analysis: forecasting and control, revised ed
    • San Francisco, USA
    • Box GEP, Jenkins GM (1970) Time series analysis: forecasting and control, revised ed. Holden-Day, San Francisco, USA
    • (1970) Holden-Day
    • Box, G.E.P.1    Jenkins, G.M.2
  • 2
    • 0003353181 scopus 로고    scopus 로고
    • Neural networks: a comprehensive foundation
    • Simon Haykin (1999) Neural networks: a comprehensive foundation, p 842
    • (1999) p 842
    • Haykin, S.1
  • 3
    • 84925489228 scopus 로고    scopus 로고
    • ANN based sediment prediction model utilizing different input scenarios
    • Afan HA, El-Shafie A, Yaseen ZM et al (2014) ANN based sediment prediction model utilizing different input scenarios. Water Resour Manag 29:1231–1245. doi:10.1007/s11269-014-0870-1
    • (2014) Water Resour Manag , vol.29 , pp. 1231-1245
    • Afan, H.A.1    El-Shafie, A.2    Yaseen, Z.M.3
  • 4
    • 33749443208 scopus 로고    scopus 로고
    • Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data
    • Alp M, Cigizoglu HK (2007) Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environ Model Softw 22:2–13. doi:10.1016/j.envsoft.2005.09.009
    • (2007) Environ Model Softw , vol.22 , pp. 2-13
    • Alp, M.1    Cigizoglu, H.K.2
  • 5
    • 84862679428 scopus 로고    scopus 로고
    • Suspended sediment modeling using genetic programming and soft computing techniques
    • Kisi O, Dailr AH, Cimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450–451:48–58. doi:10.1016/j.jhydrol.2012.05.031
    • (2012) J Hydrol , vol.450-451 , pp. 48-58
    • Kisi, O.1    Dailr, A.H.2    Cimen, M.3    Shiri, J.4
  • 6
    • 40549084354 scopus 로고    scopus 로고
    • Event-based sediment yield modeling using artificial neural network
    • Rai RK, Mathur BS (2008) Event-based sediment yield modeling using artificial neural network. Water Resour Manag 22:423–441. doi:10.1007/s11269-007-9170-3
    • (2008) Water Resour Manag , vol.22 , pp. 423-441
    • Rai, R.K.1    Mathur, B.S.2
  • 7
    • 84899860143 scopus 로고    scopus 로고
    • Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)
    • Roushangar K, Mehrabani FV, Shiri J (2014) Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs). J Hydrol 514:114–122. doi:10.1016/j.jhydrol.2014.03.065
    • (2014) J Hydrol , vol.514 , pp. 114-122
    • Roushangar, K.1    Mehrabani, F.V.2    Shiri, J.3
  • 8
    • 85001589621 scopus 로고    scopus 로고
    • Estimation of daily suspended sediment load by using wavelet conjunction models
    • Shiri J, Kişi Ö (2012) Estimation of daily suspended sediment load by using wavelet conjunction models. J Hydrol Eng 17:986–1000. doi:10.1061/(ASCE)HE.1943-5584.0000535
    • (2012) J Hydrol Eng , vol.17 , pp. 986-1000
    • Shiri, J.1    Kişi, Ö.2
  • 9
    • 20344369583 scopus 로고    scopus 로고
    • Groundwater level forecasting using artificial neural networks
    • Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309:229–240. doi:10.1016/j.jhydrol.2004.12.001
    • (2005) J Hydrol , vol.309 , pp. 229-240
    • Daliakopoulos, I.N.1    Coulibaly, P.2    Tsanis, I.K.3
  • 10
    • 79960702239 scopus 로고    scopus 로고
    • Improved water level forecasting performance by using optimal steepness coefficients in an artificial neural network
    • Sulaiman M, El-Shafie A, Karim O, Basri H (2011) Improved water level forecasting performance by using optimal steepness coefficients in an artificial neural network. Water Resour Manag 25:2525–2541. doi:10.1007/s11269-011-9824-z
    • (2011) Water Resour Manag , vol.25 , pp. 2525-2541
    • Sulaiman, M.1    El-Shafie, A.2    Karim, O.3    Basri, H.4
  • 11
    • 9444245344 scopus 로고    scopus 로고
    • Rainfall-runoff modelling using three neural network methods
    • Cigizoglu HK, Alp M (2004) Rainfall-runoff modelling using three neural network methods. Methods 3070:166–171
    • (2004) Methods , vol.3070 , pp. 166-177
    • Cigizoglu, H.K.1    Alp, M.2
  • 12
    • 70449527356 scopus 로고    scopus 로고
    • Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels
    • Unal B, Mamak M, Seckin G, Cobaner M (2010) Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels. Adv Eng Softw 41:120–129. doi:10.1016/j.advengsoft.2009.10.002
    • (2010) Adv Eng Softw , vol.41 , pp. 120-129
    • Unal, B.1    Mamak, M.2    Seckin, G.3    Cobaner, M.4
  • 13
    • 84870216315 scopus 로고    scopus 로고
    • Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia
    • Karimi S, Kisi O, Shiri J, Makarynskyy O (2013) Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia. Comput Geosci 52:50–59. doi:10.1016/j.cageo.2012.09.015
    • (2013) Comput Geosci , vol.52 , pp. 50-59
    • Karimi, S.1    Kisi, O.2    Shiri, J.3    Makarynskyy, O.4
  • 15
    • 84884588411 scopus 로고    scopus 로고
    • Basis for a streamflow forecasting system to Rincón del Bonete and Salto Grande (Uruguay)
    • Talento S, Terra R (2013) Basis for a streamflow forecasting system to Rincón del Bonete and Salto Grande (Uruguay). Theor Appl Climatol 114:73–93. doi:10.1007/s00704-012-0822-8
    • (2013) Theor Appl Climatol , vol.114 , pp. 73-93
    • Talento, S.1    Terra, R.2
  • 16
    • 0038240745 scopus 로고    scopus 로고
    • Artificial neural network approach to flood forecasting in the River Arno
    • Taylor P, Campolo M, Soldati A, Andreussi P (2003) Artificial neural network approach to flood forecasting in the River Arno. Hydrol Sci J 48:381–398
    • (2003) Hydrol Sci J , vol.48 , pp. 381-398
    • Taylor, P.1    Campolo, M.2    Soldati, A.3    Andreussi, P.4
  • 17
    • 77957016230 scopus 로고    scopus 로고
    • Application of radial basis function neural networks to short-term streamflow forecasting
    • Kagoda PA., Ndiritu J, Ntuli C, Mwaka B (2010) Application of radial basis function neural networks to short-term streamflow forecasting. Phys Chem Earth 35:571–581. doi:10.1016/j.pce.2010.07.021
    • (2010) Phys Chem Earth , vol.35 , pp. 571-581
    • Kagoda, P.A.1    Ndiritu, J.2    Ntuli, C.3    Mwaka, B.4
  • 18
    • 78149414268 scopus 로고    scopus 로고
    • Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model
    • Shiri J, Kisi O (2010) Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. J Hydrol 394:486–493. doi:10.1016/j.jhydrol.2010.10.008
    • (2010) J Hydrol , vol.394 , pp. 486-493
    • Shiri, J.1    Kisi, O.2
  • 19
    • 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 10:216–222. doi:10.1061/(ASCE)1084-0699(2005)10:3(216)
    • (2005) J Hydrol Eng , vol.10 , pp. 216-222
    • Wu, J.S.1    Han, J.2    Annambhotla, S.3    Bryant, S.4
  • 20
    • 69249208624 scopus 로고    scopus 로고
    • Enhancing inflow forecasting model at Aswan high dam utilizing radial basis neural network and upstream monitoring stations measurements
    • El-Shafie A, Abdin AE, Noureldin A, Taha MR (2009) Enhancing inflow forecasting model at Aswan high dam utilizing radial basis neural network and upstream monitoring stations measurements. Water Resour Manag 23:2289–2315. doi:10.1007/s11269-008-9382-1
    • (2009) Water Resour Manag , vol.23 , pp. 2289-2315
    • El-Shafie, A.1    Abdin, A.E.2    Noureldin, A.3    Taha, M.R.4
  • 21
    • 84930960542 scopus 로고    scopus 로고
    • Successive-station monthly streamflow prediction using different artificial neural network algorithms. Int J Environ Sci Technol
    • Danandeh Mehr A, Kahya E, Şahin A, Nazemosadat MJ (2014) Successive-station monthly streamflow prediction using different artificial neural network algorithms. Int J Environ Sci Technol. doi:10.1007/s13762-014-0613-0
    • (2014) doi:10.1007/s13762-014-0613-0
    • Danandeh Mehr, A.1    Kahya, E.2    Şahin, A.3    Nazemosadat, M.J.4
  • 22
    • 84893923942 scopus 로고    scopus 로고
    • Multilayer perceptron with different training algorithms for streamflow forecasting
    • Hosseinzadeh Talaee P (2014) Multilayer perceptron with different training algorithms for streamflow forecasting. Neural Comput Appl 24:695–703. doi:10.1007/s00521-012-1287-5
    • (2014) Neural Comput Appl , vol.24 , pp. 695-703
    • Hosseinzadeh Talaee, P.1
  • 23
    • 0033957764 scopus 로고    scopus 로고
    • Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
    • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124
    • (2000) Environ Model Softw , vol.15 , pp. 101-124
    • Maier, H.R.1    Dandy, G.C.2
  • 24
    • 0029312461 scopus 로고
    • Brown M, Harris CJ A perspective and critique of adaptive neurofuzzy systems used for modelling and control applications. 60(2):197–220
    • Brown M, Harris CJ (1995) A perspective and critique of adaptive neurofuzzy systems used for modelling and control applications. Int J Neural Syst 60(2):197–220
    • (1995) Int J Neural Syst
  • 25
    • 77956167860 scopus 로고
    • Radial basis functions, multi-variable functional interpolation and adaptive networks (No
    • Royal Signals and Radar Establishment Malvern, United Kingdom
    • Broomhead DS, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks (No. RSRE-MEMO-4148). Royal Signals and Radar Establishment Malvern, United Kingdom
    • (1988) RSRE-MEMO-4148)
    • Broomhead, D.S.1    Lowe, D.2
  • 26
    • 3142538909 scopus 로고    scopus 로고
    • Improved streamflow forecasting using self-organizing radial basis function artificial neural networks
    • Moradkhani H, Hsu K, Gupta HV, Sorooshian S (2004) Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. J Hydrol 295:246–262. doi:10.1016/j.jhydrol.2004.03.027
    • (2004) J Hydrol , vol.295 , pp. 246-262
    • Moradkhani, H.1    Hsu, K.2    Gupta, H.V.3    Sorooshian, S.4
  • 27
    • 84872738242 scopus 로고
    • Neural networks for pattern recognition
    • Bishop CM (1995) Neural networks for pattern recognition. J Am Stat Assoc. doi:10.2307/2965437
    • (1995) J Am Stat Assoc
    • Bishop, C.M.1
  • 29
    • 0003123930 scopus 로고    scopus 로고
    • Forecasting with artificial neural networks: the state of the art
    • Zhang GP, Patuwo EB, Michael YH (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62. doi:10.1016/S0169-2070(97)00044-7
    • (1998) Int J Forecast , vol.14 , pp. 35-62
    • Zhang, G.P.1    Patuwo, E.B.2    Michael, Y.H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.