메뉴 건너뛰기




Volumn 120, Issue 6, 2011, Pages 1067-1084

Intermittent reservoir daily-inflow prediction using lumped and distributed data multi-linear regression models

Author keywords

ARIMA models; Cause effect models; Combined models; India; Koyna reservoir inflow; Lumped and distributed data; Multi linear regression; Time series models

Indexed keywords

INFLOW; MULTIPLE REGRESSION; NUMERICAL MODEL; PREDICTION; RAINFALL; RIVER FLOW; TIME SERIES ANALYSIS; WATERSHED;

EID: 84555208766     PISSN: 02534126     EISSN: 0973774X     Source Type: Journal    
DOI: 10.1007/s12040-011-0127-9     Document Type: Article
Times cited : (31)

References (39)
  • 1
    • 34248202148 scopus 로고    scopus 로고
    • Artificial neural network model for synthetic streamflow generation; Water Resour
    • Ahmed J and Sarma A. K 2007 Artificial neural network model for synthetic streamflow generation; Water Resour. Mgmt. 21(6) 1015-1029.
    • (2007) Mgmt , vol.21 , Issue.6 , pp. 1015-1029
    • Ahmed, J.1    Sarma, A.K.2
  • 2
    • 0016355478 scopus 로고
    • A new look at the statistical model identification
    • Akaike H. 1974 A new look at the statistical model identification; IEEE transaction on Automatic Control AC-19(6) 716-723.
    • (1974) IEEE Transaction On Automatic Control , vol.19 , Issue.6 , pp. 716-723
    • Akaike, H.1
  • 3
    • 33947572974 scopus 로고    scopus 로고
    • A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff;
    • Aqil M, Kita I, Yano A and Nishiyama S 2007 A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff; J. Hydrol. 337(1-2) 22-34.
    • (2007) J. Hydrol , vol.337 , Issue.1-2 , pp. 22-34
    • Aqil, M.1    Kita, I.2    Yano, A.3    Nishiyama, S.4
  • 4
    • 43149114256 scopus 로고    scopus 로고
    • An application of artificial intelligence for rainfall-runoff modelling
    • Aytek A and Alp M 2008 An application of artificial intelligence for rainfall-runoff modelling; J. Earth Syst. Sci. 117(2)145-155.
    • (2008) J. Earth Syst. Sci , vol.117 , Issue.2 , pp. 145-155
    • Aytek, A.1    Alp, M.2
  • 7
    • 0027334241 scopus 로고
    • Forecasting of short-term rainfall using ARMA models;
    • Burlando P, Rosso R, Cadavid L G and Salas J D 1993 Forecasting of short-term rainfall using ARMA models; J. Hydrol. 144(1-4) 193-211.
    • (1993) J. Hydrol , vol.144 , Issue.1-4 , pp. 193-211
    • Burlando, P.1    Rosso, R.2    Cadavid, L.G.3    Salas, J.D.4
  • 8
    • 0032961025 scopus 로고    scopus 로고
    • River flood forecasting with neural network model
    • Campolo M, Andreussi P and Soldati A 1999 River flood forecasting with neural network model; Water Resour. Res. 35(4) 1191-1197.
    • (1999) Water Resour. Res , vol.35 , Issue.4 , pp. 1191-1197
    • Campolo, M.1    Andreussi, P.2    Soldati, A.3
  • 10
    • 27544472438 scopus 로고    scopus 로고
    • Comparison of several flood forecasting models in Yangtze River;
    • Chau K W, Wu C L and Li Y S 2005 Comparison of several flood forecasting models in Yangtze River; J. Hydrol. Eng. ASCE 10(6) 485-491.
    • (2005) J. Hydrol. Eng. ASCE , vol.10 , Issue.6 , pp. 485-491
    • Chau, K.W.1    Wu, C.L.2    Li, Y.S.3
  • 11
    • 0027788715 scopus 로고
    • Comparison of six rainfall-runoff modeling approaches;
    • Chiew F H S, Stewardson M J and McMahon T A 1993 Comparison of six rainfall-runoff modeling approaches; J. Hydrol. 147(1-4) 1-36.
    • (1993) J. Hydrol , vol.147 , Issue.1-4 , pp. 1-36
    • Chiew, F.H.S.1    Stewardson, M.J.2    McMahon, T.A.3
  • 12
    • 0001292308 scopus 로고
    • Definition and uses of the linear regression model
    • Diskin M H 1970 Definition and uses of the linear regression model; Water Resour. Res. 6(6) 1668-1673.
    • (1970) Water Resour. Res , vol.6 , Issue.6 , pp. 1668-1673
    • Diskin, M.H.1
  • 13
    • 0024713919 scopus 로고
    • Regression models for estimating urban storm-runoff quantity and quality in the US
    • Driver N E and Troutman B M 1989 Regression models for estimating urban storm-runoff quantity and quality in the US; J. Hydrol. 109(3-4) 221-236.
    • (1989) J. Hydrol , vol.109 , Issue.3-4 , pp. 221-236
    • Driver, N.E.1    Troutman, B.M.2
  • 14
    • 0018694430 scopus 로고
    • An evaluation on record reconstruction techniques
    • Hirsch R M 1979 An evaluation on record reconstruction techniques; Water Resour. Res. 15(6) 1781-1790.
    • (1979) Water Resour. Res , vol.15 , Issue.6 , pp. 1781-1790
    • Hirsch, R.M.1
  • 15
    • 0021504693 scopus 로고
    • Methods of fitting a straight line data: Examples in water resources
    • Hirsch R M and Gilroy E J 1984 Methods of fitting a straight line data: Examples in water resources; Water Resour. Bullet. 20(5) 705-711.
    • (1984) Water Resour. Bullet , vol.20 , Issue.5 , pp. 705-711
    • Hirsch, R.M.1    Gilroy, E.J.2
  • 16
    • 0029413797 scopus 로고
    • Artificial neural network modeling of the rainfall-runoff process
    • Hsu K L, Gupta H V and Sorooshian S 1995 Artificial neural network modeling of the 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
  • 17
    • 0033827239 scopus 로고    scopus 로고
    • Comparison of ANNs and empirical approaches for predicting watershed runoff
    • Jagdeesh A, Zhang B and Govindraju R S 2000 Comparison of ANNs and empirical approaches for predicting watershed runoff; J. Water Resour. Plann. Mgmt. ASCE 126(3)156-166.
    • (2000) J. Water Resour. Plann. Mgmt. ASCE , vol.126 , Issue.3 , pp. 156-166
    • Jagdeesh, A.1    Zhang, B.2    Govindraju, R.S.3
  • 18
    • 0037340658 scopus 로고    scopus 로고
    • Comparative analysis of event based rainfall modeling techniques - Deterministic, statistical and Artificial Neural Network;
    • Jain A and Prasad S K V 2003 Comparative analysis of event based rainfall modeling techniques - Deterministic, statistical and Artificial Neural Network; J. Hydrol. Eng. ASCE 8(2) 93-98.
    • (2003) J. Hydrol. Eng. ASCE , vol.8 , Issue.2 , pp. 93-98
    • Jain, A.1    Prasad, S.K.V.2
  • 19
    • 84555210587 scopus 로고    scopus 로고
    • Soft computing tools in rainfall-runoff modelling
    • Jothiprakash V and Magar R 2009 Soft computing tools in rainfall-runoff modelling; ISH J. Hydraul. Eng. 15(SP-1) 84-96.
    • (2009) ISH J. Hydraul. Eng , vol.15 , Issue.1 , pp. 84-96
    • Jothiprakash, V.1    Magar, R.2
  • 20
    • 84555213607 scopus 로고    scopus 로고
    • Rainfall-runoff modeling using Linear Regression - A case study of an intermittent river
    • Jothiprakash V, Magar R and Sunil K 2007 Rainfall-runoff modeling using Linear Regression - A case study of an intermittent river. J. Indian Assoc. Env. Mgmt. 34(3) 125-131.
    • (2007) J. Indian Assoc. Env. Mgmt , vol.34 , Issue.3 , pp. 125-131
    • Jothiprakash, V.1    Magar, R.2    Sunil, K.3
  • 21
    • 34548146808 scopus 로고    scopus 로고
    • Stream flow forecasting using different artificial neural network algorithms
    • Kisi O 2007 Stream flow 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
  • 22
    • 0022266291 scopus 로고
    • A comparison of rainfallrunoff modelling techniques of small upland catchments
    • Loague K M and Freeze R A 1985 A comparison of rainfallrunoff modelling techniques of small upland catchments; Water Resour. Res. 21(2) 229-248.
    • (1985) Water Resour. Res , vol.21 , Issue.2 , pp. 229-248
    • Loague, K.M.1    Freeze, R.A.2
  • 23
    • 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 J E and Sharma A 2000 A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J. Hydrol. 227(1-4) 56-65.
    • (2000) J. Hydrol , vol.227 , Issue.1-4 , pp. 56-65
    • Luk, K.C.1    Ball, J.E.2    Sharma, A.3
  • 24
    • 0031222587 scopus 로고    scopus 로고
    • Determining inputs for neural network models of multivariate time series
    • Maier H R and Dandy G C 1997 Determining inputs for neural network models of multivariate time series. Microcomput. Civil Eng. 12(5) 353-368.
    • (1997) Microcomput. Civil Eng , vol.12 , Issue.5 , pp. 353-368
    • Maier, H.R.1    Dandy, G.C.2
  • 26
    • 70350129727 scopus 로고    scopus 로고
    • Time series model for rainfall data in Jordan: Case study for using time series analysis
    • Momani M and Naill P E 2009 Time series model for rainfall data in Jordan: Case study for using time series analysis; American J. Env. Sci. 5(5) 599-604.
    • (2009) American J. Env. Sci , vol.5 , Issue.5 , pp. 599-604
    • Momani, M.1    Naill, P.E.2
  • 27
    • 0014776873 scopus 로고
    • River flow forecasting through conceptual models, part I - a discussion of principle
    • Nash J E and Sutcliffe J V 1970 River flow forecasting through conceptual models, part I - a discussion of principle. J. Hydrol. 10(3) 282-290.
    • (1970) J. Hydrol , vol.10 , Issue.3 , pp. 282-290
    • Nash, J.E.1    Sutcliffe, J.V.2
  • 28
    • 0029413130 scopus 로고
    • Models for extending stream flow
    • Raman H, Mohan S and Padalianathan 1995 Models for extending stream flow: A case study. Hydrol. Sci. J. 40 381-393.
    • (1995) A Case Study. Hydrol. Sci. J , vol.40 , pp. 381-393
    • Raman, H.1    Mohan, S.2
  • 29
    • 0018015137 scopus 로고
    • Modeling of short data description
    • Rissanen J 1978 Modeling of short data description; Automation 14 465-471.
    • (1978) Automation , vol.14 , pp. 465-471
    • Rissanen, J.1
  • 31
    • 0003775747 scopus 로고
    • Rainfall-runoff modeling. Prentice Hall, NJ
    • Singh V P 1988 Hydrologic Systems, Vol 1: Rainfall-runoff modeling. Prentice Hall, NJ.
    • (1988) Hydrologic Systems , pp. 1
    • Singh, V.P.1
  • 32
    • 0037199712 scopus 로고    scopus 로고
    • River flow forecasting: Use of phase-space recon- struction and artificial neural networks approaches
    • Sivakumar B, Jayawardena A W and Fernando T M G H 2002 River flow forecasting: Use of phase-space recon- struction and artificial neural networks approaches. J. Hydrol. 265(1-4) 225-245.
    • (2002) J. Hydrol , vol.265 , Issue.1-4 , pp. 225-245
    • Sivakumar, B.1    Jayawardena, A.W.2    Fernando, T.M.G.H.3
  • 33
    • 0027503733 scopus 로고
    • Calibration of rainfall-runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting model
    • Sorooshian S, Duan Q and Gupta V K 1993 Calibration of rainfall-runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting model. Water Resour. Res. 29(4) 1185-1194.
    • (1993) Water Resour. Res , vol.29 , Issue.4 , pp. 1185-1194
    • Sorooshian, S.1    Duan, Q.2    Gupta, V.K.3
  • 34
    • 33644636765 scopus 로고    scopus 로고
    • A comparative analysis of training methods for artificial neural network rainfall runoff models
    • Srinivasulu S and Jain A 2006 A comparative analysis of training methods for artificial neural network rainfall runoff models. Applied Soft Compt. 6(3) 295-306.
    • (2006) Applied Soft Compt , vol.6 , Issue.3 , pp. 295-306
    • Srinivasulu, S.1    Jain, A.2
  • 35
    • 0037197571 scopus 로고    scopus 로고
    • A data driven algorithm for constructing artificial neural network rainfall-runoff models
    • Sudheer K P, Gosain A K and Ramasastri K S 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
  • 37
    • 0034174356 scopus 로고    scopus 로고
    • Hydrological forecasting using neural networks
    • Thirumalaiah K and Deo M C 2000 Hydrological forecasting using neural networks. J. Hydrol Eng. ASCE 5(2) 180-189.
    • (2000) J. Hydrol Eng. ASCE , vol.5 , Issue.2 , pp. 180-189
    • Thirumalaiah, K.1    Deo, M.C.2
  • 38
    • 0034694775 scopus 로고    scopus 로고
    • Comparison of short-term rainfall prediction models for real-time flood forecasting
    • Toth E, Brath and Montanari A 2000 Comparison of short-term rainfall prediction models for real-time flood forecasting. J. Hydrol. 239(1-4) 132-147.
    • (2000) J. Hydrol , vol.239 , Issue.1-4 , pp. 132-147
    • Toth, E.1    Brath2    Montanari, A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.