메뉴 건너뛰기




Volumn 66, Issue 7, 2012, Pages 2031-2045

Flow estimations for the Sohu Stream using artificial neural networks

Author keywords

Areal precipitation; Feed forward back propagation method; Spatial interpolation; Streamflow forecasting

Indexed keywords

AREAL PRECIPITATION; COEFFICIENT OF DETERMINATION; CURRENT FLOWS; FEED-FORWARD BACK-PROPAGATION METHODS; FLOW DATA; FLOW ESTIMATION; HIDDEN LAYERS; INPUT LAYERS; INPUT VARIABLES; OUTPUT LAYER; RAINFALL-RUNOFF RELATIONSHIP; REAL WORLD SITUATIONS; SPATIAL INTERPOLATION; STREAMFLOW FORECASTING;

EID: 84864109362     PISSN: 18666280     EISSN: 18666299     Source Type: Journal    
DOI: 10.1007/s12665-011-1428-7     Document Type: Article
Times cited : (30)

References (56)
  • 1
    • 63649137913 scopus 로고    scopus 로고
    • Modelling of rainfall-runoff relation with different ANN methods
    • (in Turkish)
    • Alp M, Cigizoglu HK (2004) Modelling of rainfall-runoff relation with different ANN methods. ITU Dergisi/d Muhendislik 3(1): 80-88 (in Turkish).
    • (2004) ITU Dergisi/D Muhendislik , vol.3 , Issue.1 , pp. 80-88
    • Alp, M.1    Cigizoglu, H.K.2
  • 2
    • 1442291113 scopus 로고    scopus 로고
    • Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models
    • Anctil F, Perrin C, Andreassian V (2004) Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models. J Environ Models Softw 19: 357-368.
    • (2004) J Environ Models Softw , vol.19 , pp. 357-368
    • Anctil, F.1    Perrin, C.2    Andreassian, V.3
  • 4
    • 84864092075 scopus 로고    scopus 로고
    • Application of runoff and sediment yield models by geographic information system
    • Apaydi{dotless}n H, Ozturk F (2003) Application of runoff and sediment yield models by geographic information system. J Agric Sci 9(4): 381-389.
    • (2003) J Agric Sci , vol.9 , Issue.4 , pp. 381-389
    • Apaydin, H.1    Ozturk, F.2
  • 5
    • 13844266104 scopus 로고    scopus 로고
    • Spatial interpolation techniques for climate data in the GAP region in Turkey
    • Apaydin H, Sonmez FK, Yildirim YE (2004) Spatial interpolation techniques for climate data in the GAP region in Turkey. Clim Res 28: 31-40.
    • (2004) Clim Res , vol.28 , pp. 31-40
    • Apaydin, H.1    Sonmez, F.K.2    Yildirim, Y.E.3
  • 8
    • 0036715707 scopus 로고    scopus 로고
    • Performance of neural networks in daily streamflow forecasting
    • Brikundavyi S, Labib R, Trung HT, Rousselle J (2002) Performance of neural networks in daily streamflow forecasting. J Hydrol Eng 7(5): 392-398.
    • (2002) J Hydrol Eng , vol.7 , Issue.5 , pp. 392-398
    • Brikundavyi, S.1    Labib, R.2    Trung, H.T.3    Rousselle, J.4
  • 9
    • 0032688155 scopus 로고    scopus 로고
    • River flood forecasting with a neural network model
    • Campolo M, Andreussi P, Soldati A (1999) River flood forecasting with a neural network model. Water Resour Res 35: 1191-1197.
    • (1999) Water Resour Res , vol.35 , pp. 1191-1197
    • Campolo, M.1    Andreussi, P.2    Soldati, A.3
  • 10
    • 77953630861 scopus 로고    scopus 로고
    • Hazard assessment model for debris flow prediction
    • Chang TC, Wang ZY, Chien YH (2010) Hazard assessment model for debris flow prediction. Environ Earth Sci 60: 1619-1630.
    • (2010) Environ Earth Sci , vol.60 , pp. 1619-1630
    • Chang, T.C.1    Wang, Z.Y.2    Chien, Y.H.3
  • 11
    • 0036171294 scopus 로고    scopus 로고
    • Suspended sediment estimation and forecasting using artificial neural networks
    • Cigizoglu HK (2002a) Suspended sediment estimation and forecasting using artificial neural networks. Turk J Eng Environ Sci 26: 15-25.
    • (2002) Turk J Eng Environ Sci , vol.26 , pp. 15-25
    • Cigizoglu, H.K.1
  • 12
    • 0036171309 scopus 로고    scopus 로고
    • Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves
    • Cigizoglu HK (2002b) Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves. Turk J Eng Environ Sci 26: 27-36.
    • (2002) Turk J Eng Environ Sci , vol.26 , pp. 27-36
    • Cigizoglu, H.K.1
  • 13
    • 0038240755 scopus 로고    scopus 로고
    • Estimation, forecasting and extrapolation of flow data by artificial neural networks
    • Cigizoglu HK (2003a) Estimation, forecasting and extrapolation of flow data by artificial neural networks. J Hydrol Sci 48(3): 349-361.
    • (2003) J Hydrol Sci , vol.48 , Issue.3 , pp. 349-361
    • Cigizoglu, H.K.1
  • 14
    • 0037783429 scopus 로고    scopus 로고
    • Incorporation of ARMA models into flow forecasting by artificial neural networks
    • Cigizoglu HK (2003b) Incorporation of ARMA models into flow forecasting by artificial neural networks. Environmetrics 14(4): 417-427.
    • (2003) Environmetrics , vol.14 , Issue.4 , pp. 417-427
    • Cigizoglu, H.K.1
  • 15
    • 27944503514 scopus 로고    scopus 로고
    • Generalized regression neural networks in monthly flow forecasting
    • Cigizoglu HK (2005) Generalized regression neural networks in monthly flow forecasting. Civil Eng Environ Syst 22(2): 71-84.
    • (2005) Civil Eng Environ Syst , vol.22 , Issue.2 , pp. 71-84
    • Cigizoglu, H.K.1
  • 16
    • 12544253180 scopus 로고    scopus 로고
    • Flow prediction by two back propagation techniques using k-fold partitioning of neural network training data
    • Cigizoglu HK, Kisi O (2005) Flow prediction by two back propagation techniques using k-fold partitioning of neural network training data. J Nord Hydrol 36(1): 1-16.
    • (2005) J Nord Hydrol , vol.36 , Issue.1 , pp. 1-16
    • Cigizoglu, H.K.1    Kisi, O.2
  • 17
    • 0342532702 scopus 로고    scopus 로고
    • Real time neural network based forecasting system for hydropower reservoirs
    • Technologies for decision making in civil engineering, University of Quebec, Montreal, Canada
    • Coulibaly P, Anctil F, Bobée B (1998) Real time neural network based forecasting system for hydropower reservoirs. In: Proceedings of the first international conference on new information. Technologies for decision making in civil engineering, University of Quebec, Montreal, Canada, pp 1001-1011.
    • (1998) Proceedings of the first international conference on new information , pp. 1001-1011
    • Coulibaly, P.1    Anctil, F.2    Bobée, B.3
  • 18
    • 34249810384 scopus 로고    scopus 로고
    • Multi-objective performance comparison of an artificial neural network and a conceptual rainfall-runoff model
    • De Vos NJ, Rientjes THM (2007) Multi-objective performance comparison of an artificial neural network and a conceptual rainfall-runoff model. Hydrol Sci J 52(3): 397-413.
    • (2007) Hydrol Sci J , vol.52 , Issue.3 , pp. 397-413
    • de Vos, N.J.1    Rientjes, T.H.M.2
  • 19
  • 20
    • 77955137293 scopus 로고    scopus 로고
    • GIS-based urban rainfall-runoff modeling using an automatic catchment-discretization approach: a case study in Macau
    • Dongquan Z, Jining C, Haozheng W, Qingyuan T, Shangbing C, Zheng S (2009) GIS-based urban rainfall-runoff modeling using an automatic catchment-discretization approach: a case study in Macau. Environ Earth Sci 59: 465-472.
    • (2009) Environ Earth Sci , vol.59 , pp. 465-472
    • Dongquan, Z.1    Jining, C.2    Haozheng, W.3    Qingyuan, T.4    Shangbing, C.5    Zheng, S.6
  • 21
    • 79951578869 scopus 로고    scopus 로고
    • The simulation of snow melt runoff in the ungauged Kaidu River Basin of Tian Shan Mountains, China
    • Dou Y, Chen X, Bao A, Li L (2011) The simulation of snow melt runoff in the ungauged Kaidu River Basin of Tian Shan Mountains, China. Environ Earth Sci 62: 1039-1045.
    • (2011) Environ Earth Sci , vol.62 , pp. 1039-1045
    • Dou, Y.1    Chen, X.2    Bao, A.3    Li, L.4
  • 22
    • 84864101269 scopus 로고    scopus 로고
    • EIE, Ankara (in Turkish)
    • EIE (2006) EIE annual flow report, Ankara (in Turkish).
    • (2006) EIE annual flow report
  • 23
    • 37549038684 scopus 로고    scopus 로고
    • Neural networks to simulate regional ground water levels affected by human activities
    • Feng S, Kang S, Huo Z, Chen S, Mao X (2008) Neural networks to simulate regional ground water levels affected by human activities. Ground Water 46(1): 80-90.
    • (2008) Ground Water , vol.46 , Issue.1 , pp. 80-90
    • Feng, S.1    Kang, S.2    Huo, Z.3    Chen, S.4    Mao, X.5
  • 24
    • 0032123339 scopus 로고    scopus 로고
    • Runoff forecasting using RBF networks with OLS algorithm
    • Fernando DAK, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng 3(3): 203-209.
    • (1998) J Hydrol Eng , vol.3 , Issue.3 , pp. 203-209
    • Fernando, D.A.K.1    Jayawardena, A.W.2
  • 26
    • 48649086325 scopus 로고    scopus 로고
    • A systematic approach to the input determination for neural network rainfall-runoff models
    • Gwo-Fong L, Chen GR (2008) A systematic approach to the input determination for neural network rainfall-runoff models. Hydrol Process 22(14): 2524-2530.
    • (2008) Hydrol Process , vol.22 , Issue.14 , pp. 2524-2530
    • Gwo-Fong, L.1    Chen, G.R.2
  • 27
    • 0029413797 scopus 로고
    • Artificial neural network modelling of the rainfall runoff process
    • Hsu K, Gupta HV, 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
  • 28
    • 84864085950 scopus 로고    scopus 로고
    • Development of integrated sediment rating curves using ANNs
    • Jain SK (2001) Development of integrated sediment rating curves using ANNs. J Hydraul Eng 9(1): 60-63.
    • (2001) J Hydraul Eng , vol.9 , Issue.1 , pp. 60-63
    • Jain, S.K.1
  • 29
    • 0033197895 scopus 로고    scopus 로고
    • Application of ANN for reservoir inflow prediction and operation
    • Jain SK, Das D, Srivastava DK (1999) Application of ANN for reservoir inflow prediction and operation. J Water Resour Plan Manag 125(5): 263-271.
    • (1999) J Water Resour Plan Manag , vol.125 , Issue.5 , pp. 263-271
    • Jain, S.K.1    Das, D.2    Srivastava, D.K.3
  • 30
    • 0037340658 scopus 로고    scopus 로고
    • Comparative analysis of event-based rainfall-runoff modeling techniques-deterministic, statistical, and artificial neural networks
    • Jain A, Indurthy SKVP (2003) Comparative analysis of event-based rainfall-runoff modeling techniques-deterministic, statistical, and artificial neural networks. J Hydrol Eng 8(2): 93-98.
    • (2003) J Hydrol Eng , vol.8 , Issue.2 , pp. 93-98
    • Jain, A.1    Indurthy, S.K.V.P.2
  • 32
    • 84864083000 scopus 로고    scopus 로고
    • KHGM, (1253) Basin research report No. 57, Ankara (in Turkish)
    • KHGM (1999) KHGM Findikli-Sohu Creek (1253) Basin research report No. 57, Ankara (in Turkish).
    • (1999) KHGM Findikli-Sohu Creek
  • 33
    • 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 9(1): 60-63.
    • (2004) J Hydrol Eng , vol.9 , Issue.1 , pp. 60-63
    • Kisi, O.1
  • 35
    • 0003265255 scopus 로고    scopus 로고
    • Advantages of unit hydrograph derivation by neural networks
    • Copenhagen
    • Lange N (1998) Advantages of unit hydrograph derivation by neural networks. In: Hydroinformatics conference, Copenhagen.
    • (1998) Hydroinformatics conference
    • Lange, N.1
  • 36
    • 0029748915 scopus 로고    scopus 로고
    • A neural network model of rainfall runoff using radial basis functions
    • Mason JC, Price RK, Tem'me A (1996) A neural network model of rainfall runoff using radial basis functions. J Hydraul Res 34(4): 537-548.
    • (1996) J Hydraul Res , vol.34 , Issue.4 , pp. 537-548
    • Mason, J.C.1    Price, R.K.2    Tem'me, A.3
  • 37
    • 0000764820 scopus 로고
    • Determination of the size of sewers
    • McMath RE (1887) Determination of the size of sewers. Trans Am Soc Civil Eng 16: 179-190.
    • (1887) Trans Am Soc Civil Eng , vol.16 , pp. 179-190
    • McMath, R.E.1
  • 38
    • 0030159380 scopus 로고    scopus 로고
    • Artificial neural networks as rainfall runoff models
    • Minns AW, Hall MJ (1996) Artificial neural networks as rainfall runoff models. J Hydrol Sci 41(3): 399-417.
    • (1996) J Hydrol Sci , vol.41 , Issue.3 , pp. 399-417
    • Minns, A.W.1    Hall, M.J.2
  • 39
    • 0014776873 scopus 로고
    • River flow forecasting through conceptual models. Part I: a discussion of principles
    • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. Part I: a discussion of principles. J Hydrol 10: 282-290.
    • (1970) J Hydrol , vol.10 , pp. 282-290
    • Nash, J.E.1    Sutcliffe, J.V.2
  • 42
    • 0029413038 scopus 로고
    • Multivariate modeling of water resources time series using artificial neural networks
    • Raman H, Sunilkumar N (1995) Multivariate modeling of water resources time series using artificial neural networks. J Hydrol Sci 40(2): 145-163.
    • (1995) J Hydrol Sci , vol.40 , Issue.2 , pp. 145-163
    • Raman, H.1    Sunilkumar, N.2
  • 43
    • 0027464578 scopus 로고
    • Neural network-based screening for groundwater reclamation under uncertainty
    • Ranjithan S, Eheart JW, Garrett JH (1993) Neural network-based screening for groundwater reclamation under uncertainty. Water Resour Res 29(3): 563-574.
    • (1993) Water Resour Res , vol.29 , Issue.3 , pp. 563-574
    • Ranjithan, S.1    Eheart, J.W.2    Garrett, J.H.3
  • 44
    • 0028174533 scopus 로고
    • Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling
    • Rogers LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour Res 30(2): 457-481.
    • (1994) Water Resour Res , vol.30 , Issue.2 , pp. 457-481
    • Rogers, L.L.1    Dowla, F.U.2
  • 46
    • 84864087649 scopus 로고    scopus 로고
    • Simulation of Savalan irrigation reservoir by using artificial neural networks
    • Sattari MT, Fakher-Fard A, Docherkhesaz A, Ozturk F (2007) Simulation of Savalan irrigation reservoir by using artificial neural networks. J Hydrol 214: 32-48.
    • (2007) J Hydrol , vol.214 , pp. 32-48
    • Sattari, M.T.1    Fakher-Fard, A.2    Docherkhesaz, A.3    Ozturk, F.4
  • 47
    • 71149084920 scopus 로고    scopus 로고
    • Operation analysis of Eleviyan irrigation reservoir dam by optimization and stochastic simulation
    • Sattari MT, Apaydin H, Ozturk F (2009) Operation analysis of Eleviyan irrigation reservoir dam by optimization and stochastic simulation. Stoch Env Res Risk Assess 23(8): 1187-1201.
    • (2009) Stoch Env Res Risk Assess , vol.23 , Issue.8 , pp. 1187-1201
    • Sattari, M.T.1    Apaydin, H.2    Ozturk, F.3
  • 48
    • 44649197249 scopus 로고    scopus 로고
    • Estimating daily mean sea level heights using artificial neural networks
    • Sertel E, Cigizoglu HK, Sanli DU (2008) Estimating daily mean sea level heights using artificial neural networks. J Coast Res 24(3): 727-734.
    • (2008) J Coast Res , vol.24 , Issue.3 , pp. 727-734
    • Sertel, E.1    Cigizoglu, H.K.2    Sanli, D.U.3
  • 49
    • 0342506462 scopus 로고    scopus 로고
    • Application of a neural network technique to rainfall-runoff modeling
    • Shamseldin AY (1997) Application of a neural network technique to rainfall-runoff modeling. J Hydrol 199: 272-294.
    • (1997) J Hydrol , vol.199 , pp. 272-294
    • Shamseldin, A.Y.1
  • 52
    • 0033167344 scopus 로고    scopus 로고
    • Rainfall runoff modeling using artificial neural networks
    • Tokar AS, Johnson PA (1999) Rainfall runoff modeling using artificial neural networks. J Hydrol Eng 4(3): 232-239.
    • (1999) J Hydrol Eng , vol.4 , Issue.3 , pp. 232-239
    • Tokar, A.S.1    Johnson, P.A.2
  • 54
    • 0003956158 scopus 로고
    • United States Department of Agriculture, Technical release 55 (TR-55), second edn. Natural Resources Conservation Service, Conservation Engineering Division
    • United States Department of Agriculture (1986) Urban hydrology for small watersheds. Technical release 55 (TR-55), second edn. Natural Resources Conservation Service, Conservation Engineering Division.
    • (1986) Urban hydrology for small watersheds
  • 56
    • 0033019602 scopus 로고    scopus 로고
    • Short-term stream flow forecasting using artificial neural networks
    • Zealand CM, Burn DH, Simonovic SP (1999) Short-term stream flow forecasting using artificial neural networks. J Hydrol 214: 32-48.
    • (1999) J Hydrol , vol.214 , pp. 32-48
    • Zealand, C.M.1    Burn, D.H.2    Simonovic, S.P.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.