메뉴 건너뛰기




Volumn 538, Issue , 2016, Pages 754-766

Streamflow forecasting using functional regression

Author keywords

Functional data; Functional linear models; Regression; Streamflow hydrograph

Indexed keywords

FORECASTING; NEURAL NETWORKS; STREAM FLOW;

EID: 84968739633     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2016.04.048     Document Type: Article
Times cited : (35)

References (68)
  • 1
    • 14344261493 scopus 로고    scopus 로고
    • Generalisation for neural networks through data sampling and training procedures, with applications to streamflow predictions
    • Anctil F., Lauzon N. Generalisation for neural networks through data sampling and training procedures, with applications to streamflow predictions. Hydrol. Earth Syst. Sci. Discuss. 2004, 8:940-958.
    • (2004) Hydrol. Earth Syst. Sci. Discuss. , vol.8 , pp. 940-958
    • Anctil, F.1    Lauzon, N.2
  • 2
    • 79551713734 scopus 로고    scopus 로고
    • Spatio-temporal functional regression on paleoecological data
    • Bel L., Bar-Hen A., Petit R., Cheddadi R. Spatio-temporal functional regression on paleoecological data. J. Appl. Stat. 2011, 38:695-704.
    • (2011) J. Appl. Stat. , vol.38 , pp. 695-704
    • Bel, L.1    Bar-Hen, A.2    Petit, R.3    Cheddadi, R.4
  • 5
    • 0032356568 scopus 로고    scopus 로고
    • Smoothing spline models for the analysis of nested and crossed samples of curves
    • Brumback B.A., Rice J.A. Smoothing spline models for the analysis of nested and crossed samples of curves. J. Am. Stat. Assoc. 1998, 93:961-976.
    • (1998) J. Am. Stat. Assoc. , vol.93 , pp. 961-976
    • Brumback, B.A.1    Rice, J.A.2
  • 7
    • 0242490488 scopus 로고    scopus 로고
    • Functional approaches for predicting land use with the temporal evolution of coarse resolution remote sensing data
    • Cardot H., Faivre R., Goulard M. Functional approaches for predicting land use with the temporal evolution of coarse resolution remote sensing data. J. Appl. Stat. 2003, 30:1185-1199.
    • (2003) J. Appl. Stat. , vol.30 , pp. 1185-1199
    • Cardot, H.1    Faivre, R.2    Goulard, M.3
  • 9
    • 0041459525 scopus 로고    scopus 로고
    • Spline estimators for the functional linear model
    • Cardot H., Ferraty F., Sarda P. Spline estimators for the functional linear model. Stat. Sinica 2003, 13:571-592.
    • (2003) Stat. Sinica , vol.13 , pp. 571-592
    • Cardot, H.1    Ferraty, F.2    Sarda, P.3
  • 10
    • 27544472438 scopus 로고    scopus 로고
    • Comparison of several flood forecasting models in yangtze river
    • Chau K., Wu C., Li Y. Comparison of several flood forecasting models in yangtze river. J. Hydrol. Eng. 2005, 10:485-491.
    • (2005) J. Hydrol. Eng. , vol.10 , pp. 485-491
    • Chau, K.1    Wu, C.2    Li, Y.3
  • 11
    • 84915818949 scopus 로고    scopus 로고
    • Regional frequency analysis at ungauged sites with the generalized additive model
    • Chebana F., Charron C., Ouarda T.B.M.J., Martel B. Regional frequency analysis at ungauged sites with the generalized additive model. J. Hydrometeorol. 2014, 15:2418-2428.
    • (2014) J. Hydrometeorol. , vol.15 , pp. 2418-2428
    • Chebana, F.1    Charron, C.2    Ouarda, T.B.M.J.3    Martel, B.4
  • 12
    • 84859886984 scopus 로고    scopus 로고
    • Exploratory functional flood frequency analysis and outlier detection
    • Chebana F., Dabo-Niang S., Ouarda T.B.M.J. Exploratory functional flood frequency analysis and outlier detection. Water Resour. Res. 2012, 48:W04514.
    • (2012) Water Resour. Res. , vol.48 , pp. W04514
    • Chebana, F.1    Dabo-Niang, S.2    Ouarda, T.B.M.J.3
  • 13
    • 84901049442 scopus 로고    scopus 로고
    • Dynamical functional prediction and classification, with application to traffic flow prediction
    • Chiou J.-M. Dynamical functional prediction and classification, with application to traffic flow prediction. Ann. Appl. Stat. 2012, 6:1588-1614.
    • (2012) Ann. Appl. Stat. , vol.6 , pp. 1588-1614
    • Chiou, J.-M.1
  • 14
    • 38349000857 scopus 로고    scopus 로고
    • Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques
    • Chokmani K., Ouarda T.B.M.J., Hamilton S., Ghedira M.H., Gingras H. Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques. J. Hydrol. 2008, 349:383-396.
    • (2008) J. Hydrol. , vol.349 , pp. 383-396
    • Chokmani, K.1    Ouarda, T.B.M.J.2    Hamilton, S.3    Ghedira, M.H.4    Gingras, H.5
  • 15
    • 0036622664 scopus 로고    scopus 로고
    • Linear functional regression: the case of fixed design and functional response
    • Cuevas A., Febrero M., Fraiman R. Linear functional regression: the case of fixed design and functional response. Can. J. Stat. 2002, 30:285-300.
    • (2002) Can. J. Stat. , vol.30 , pp. 285-300
    • Cuevas, A.1    Febrero, M.2    Fraiman, R.3
  • 17
    • 0036829580 scopus 로고    scopus 로고
    • The inclusion of exogenous variables in functional autoregressive ozone forecasting
    • Damon J., Guillas S. The inclusion of exogenous variables in functional autoregressive ozone forecasting. Environmetrics 2002, 13:759-774.
    • (2002) Environmetrics , vol.13 , pp. 759-774
    • Damon, J.1    Guillas, S.2
  • 18
    • 77952396887 scopus 로고    scopus 로고
    • Statistics for spatial functional data: some recent contributions
    • Delicado P., Giraldo R., Comas C., Mateu J. Statistics for spatial functional data: some recent contributions. Environmetrics 2010, 21:224-239.
    • (2010) Environmetrics , vol.21 , pp. 224-239
    • Delicado, P.1    Giraldo, R.2    Comas, C.3    Mateu, J.4
  • 19
    • 0043095487 scopus 로고    scopus 로고
    • Long-range forecasting of the Nile River flows using climatic forcing
    • Eldaw A.K., Salas J.D., Garcia L.A. Long-range forecasting of the Nile River flows using climatic forcing. J. Appl. Meteorol. 2003, 42:890-904.
    • (2003) J. Appl. Meteorol. , vol.42 , pp. 890-904
    • Eldaw, A.K.1    Salas, J.D.2    Garcia, L.A.3
  • 22
    • 0027009397 scopus 로고
    • Improved techniques in regression-based streamflow volume forecasting
    • Garen D. Improved techniques in regression-based streamflow volume forecasting. J. Water Resour. Plann. Manage. 1992, 118:654-670.
    • (1992) J. Water Resour. Plann. Manage. , vol.118 , pp. 654-670
    • Garen, D.1
  • 23
    • 77956261937 scopus 로고    scopus 로고
    • Functional clustering and linear regression for peak load forecasting
    • Goia A., May C., Fusai G. Functional clustering and linear regression for peak load forecasting. Int. J. Forecast. 2010, 26:700-711.
    • (2010) Int. J. Forecast. , vol.26 , pp. 700-711
    • Goia, A.1    May, C.2    Fusai, G.3
  • 26
    • 33748065484 scopus 로고
    • Spurious regressions in econometrics
    • Granger C.W.J., Newbold P. Spurious regressions in econometrics. J. Econometrics 1974, 2:111-120.
    • (1974) J. Econometrics , vol.2 , pp. 111-120
    • Granger, C.W.J.1    Newbold, P.2
  • 27
    • 84873566710 scopus 로고    scopus 로고
    • Applicability of monte carlo cross validation technique for model development and validation using generalised least squares regression
    • Haddad K., Rahman A., A Zaman M., Shrestha S. Applicability of monte carlo cross validation technique for model development and validation using generalised least squares regression. J. Hydrol. 2013, 482:119-128.
    • (2013) J. Hydrol. , vol.482 , pp. 119-128
    • Haddad, K.1    Rahman, A.2    A Zaman, M.3    Shrestha, S.4
  • 28
    • 0000467952 scopus 로고
    • A statistical view of some chemometrics regression tools: discussion
    • Hastie T., Mallows C. A statistical view of some chemometrics regression tools: discussion. Technometrics 1993, 35:140-143.
    • (1993) Technometrics , vol.35 , pp. 140-143
    • Hastie, T.1    Mallows, C.2
  • 29
    • 84972488102 scopus 로고
    • Generalized additive models
    • Hastie T., Tibshirani R. Generalized additive models. Stat. Sci. 1986, 1:297-310.
    • (1986) Stat. Sci. , vol.1 , pp. 297-310
    • Hastie, T.1    Tibshirani, R.2
  • 31
    • 84942484786 scopus 로고
    • Ridge regression: biased estimation for nonorthogonal problems
    • Hoerl A.E., Kennard R.W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 1970, 12:55-67.
    • (1970) Technometrics , vol.12 , pp. 55-67
    • Hoerl, A.E.1    Kennard, R.W.2
  • 32
    • 0345800480 scopus 로고    scopus 로고
    • Nonstationary time series, cointegration, and the principle of the common cause
    • Hoover K.D. Nonstationary time series, cointegration, and the principle of the common cause. Br. J. Philos. Sci. 2003, 54:527-551.
    • (2003) Br. J. Philos. Sci. , vol.54 , pp. 527-551
    • Hoover, K.D.1
  • 34
    • 84900560768 scopus 로고    scopus 로고
    • Kriging with external drift for functional data for air quality monitoring
    • Ignaccolo R., Mateu J., Giraldo R. Kriging with external drift for functional data for air quality monitoring. Stoch. Env. Res. Risk Assess. 2014, 28:1171-1186.
    • (2014) Stoch. Env. Res. Risk Assess. , vol.28 , pp. 1171-1186
    • Ignaccolo, R.1    Mateu, J.2    Giraldo, R.3
  • 35
    • 84906077779 scopus 로고    scopus 로고
    • Penalized Function-on-function Regression
    • Johns Hopkins University, Dept. of Biostatistics Working Papers
    • Ivanescu, A.E., Staicu, A.-M., Scheipl, F., Greven, S., 2014. Penalized Function-on-function Regression. Johns Hopkins University, Dept. of Biostatistics Working Papers.
    • (2014)
    • Ivanescu, A.E.1    Staicu, A.-M.2    Scheipl, F.3    Greven, S.4
  • 36
    • 78650221431 scopus 로고    scopus 로고
    • Recent history functional linear models for sparse longitudinal data
    • Kim K., Sentürk D., Li R. Recent history functional linear models for sparse longitudinal data. J. Stat. Plan. Infer. 2011, 141:1554-1566.
    • (2011) J. Stat. Plan. Infer. , vol.141 , pp. 1554-1566
    • Kim, K.1    Sentürk, D.2    Li, R.3
  • 37
    • 34548146808 scopus 로고    scopus 로고
    • Streamflow forecasting using different artificial neural network algorithms
    • Kisi O. Streamflow forecasting using different artificial neural network algorithms. J. Hydrol. Eng. 2007, 12:532-539.
    • (2007) J. Hydrol. Eng. , vol.12 , pp. 532-539
    • Kisi, O.1
  • 38
    • 71649094330 scopus 로고    scopus 로고
    • Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting
    • Kisi O. Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting. Hydrol. Process. 2009, 23:3583-3597.
    • (2009) Hydrol. Process. , vol.23 , pp. 3583-3597
    • Kisi, O.1
  • 39
    • 40849130727 scopus 로고    scopus 로고
    • Short-term streamflow forecasting with global climate change implications - a comparative study between genetic programming and neural network models
    • Makkeasorn A., Chang N.B., Zhou X. Short-term streamflow forecasting with global climate change implications - a comparative study between genetic programming and neural network models. J. Hydrol. 2008, 352:336-354.
    • (2008) J. Hydrol. , vol.352 , pp. 336-354
    • Makkeasorn, A.1    Chang, N.B.2    Zhou, X.3
  • 40
    • 0242595929 scopus 로고    scopus 로고
    • The historical functional linear model
    • Malfait N., Ramsay J.O. The historical functional linear model. Can. J. Stat. 2003, 31:115-128.
    • (2003) Can. J. Stat. , vol.31 , pp. 115-128
    • Malfait, N.1    Ramsay, J.O.2
  • 42
    • 42949111568 scopus 로고    scopus 로고
    • Inferring gene expression dynamics via functional regression analysis
    • Müller H.-G., Chiou J.-M., Leng X. Inferring gene expression dynamics via functional regression analysis. BMC Bioinformatics 2008, 9:60.
    • (2008) BMC Bioinformatics , vol.9 , pp. 60
    • Müller, H.-G.1    Chiou, J.-M.2    Leng, X.3
  • 43
    • 0034554793 scopus 로고    scopus 로고
    • Regional flood peak and volume estimation in northern canadian basin
    • Ouarda T., Haché M., Bruneau P., Bobée B. Regional flood peak and volume estimation in northern canadian basin. J. Cold Reg. Eng. 2000, 14:176-191.
    • (2000) J. Cold Reg. Eng. , vol.14 , pp. 176-191
    • Ouarda, T.1    Haché, M.2    Bruneau, P.3    Bobée, B.4
  • 44
    • 0000308535 scopus 로고
    • Time series regression with a unit root
    • Phillips P.C.B. Time series regression with a unit root. Econometrica 1987, 55:277-301.
    • (1987) Econometrica , vol.55 , pp. 277-301
    • Phillips, P.C.B.1
  • 45
    • 0000519382 scopus 로고
    • When the data are functions
    • Ramsay J. When the data are functions. Psychometrika 1982, 47:379-396.
    • (1982) Psychometrika , vol.47 , pp. 379-396
    • Ramsay, J.1
  • 49
    • 0004130505 scopus 로고    scopus 로고
    • Functional Data Analysis
    • Wiley Online Library
    • Ramsay J.O., Silverman B. Functional Data Analysis. second ed. 2005, Wiley Online Library.
    • (2005) second ed.
    • Ramsay, J.O.1    Silverman, B.2
  • 51
    • 0037197606 scopus 로고    scopus 로고
    • Functional data analysis with application to periodically stimulated foetal heart rate data. II: functional logistic regression
    • Ratcliffe S.J., Heller G.Z., Leader L.R. Functional data analysis with application to periodically stimulated foetal heart rate data. II: functional logistic regression. Stat. Med. 2002, 21:1115-1127.
    • (2002) Stat. Med. , vol.21 , pp. 1115-1127
    • Ratcliffe, S.J.1    Heller, G.Z.2    Leader, L.R.3
  • 52
    • 0037197639 scopus 로고    scopus 로고
    • Functional data analysis with application to periodically stimulated foetal heart rate data. I: functional regression
    • Ratcliffe S.J., Leader L.R., Heller G.Z. Functional data analysis with application to periodically stimulated foetal heart rate data. I: functional regression. Stat. Med. 2002, 21:1103-1114.
    • (2002) Stat. Med. , vol.21 , pp. 1103-1114
    • Ratcliffe, S.J.1    Leader, L.R.2    Heller, G.Z.3
  • 53
    • 84878148042 scopus 로고    scopus 로고
    • Wavelet regression models for predicting flood stages in rivers: a case study in eastern india
    • Sahay R.R., Sehgal V. Wavelet regression models for predicting flood stages in rivers: a case study in eastern india. J. Flood Risk Manage. 2013, 6:146-155.
    • (2013) J. Flood Risk Manage. , vol.6 , pp. 146-155
    • Sahay, R.R.1    Sehgal, V.2
  • 55
    • 67449114030 scopus 로고    scopus 로고
    • Functional regression: a new model for predicting market penetration of new products
    • Sood A., James G.M., Tellis G.J. Functional regression: a new model for predicting market penetration of new products. Market. Sci. 2009, 28:36-51.
    • (2009) Market. Sci. , vol.28 , pp. 36-51
    • Sood, A.1    James, G.M.2    Tellis, G.J.3
  • 56
    • 84901697403 scopus 로고    scopus 로고
    • Quantifying flow-ecology relationships with functional linear models
    • Stewart-Koster B., Olden J.D., Gido K.B. Quantifying flow-ecology relationships with functional linear models. Hydrol. Sci. J. 2014, 59:629-644.
    • (2014) Hydrol. Sci. J. , vol.59 , pp. 629-644
    • Stewart-Koster, B.1    Olden, J.D.2    Gido, K.B.3
  • 57
    • 0000629975 scopus 로고
    • Cross-validatory choice and assessment of statistical predictions
    • Stone M. Cross-validatory choice and assessment of statistical predictions. J. Roy. Stat. Soc. Ser. B (Methodol.) 1974, 36:111-147.
    • (1974) J. Roy. Stat. Soc. Ser. B (Methodol.) , vol.36 , pp. 111-147
    • Stone, M.1
  • 58
    • 81555219287 scopus 로고    scopus 로고
    • Comparing rainfall patterns between regions in peninsular Malaysia via a functional data analysis technique
    • Suhaila J., Jemain A.A., Hamdan M.F., Wan Zin W.Z. Comparing rainfall patterns between regions in peninsular Malaysia via a functional data analysis technique. J. Hydrol. 2011, 411:197-206.
    • (2011) J. Hydrol. , vol.411 , pp. 197-206
    • Suhaila, J.1    Jemain, A.A.2    Hamdan, M.F.3    Wan Zin, W.Z.4
  • 60
    • 34548175731 scopus 로고    scopus 로고
    • Long lead-time forecasting of U.S. streamflow using partial least squares regression
    • Tootle G., Singh A., Piechota T., Farnham I. Long lead-time forecasting of U.S. streamflow using partial least squares regression. J. Hydrol. Eng. 2007, 12:442-451.
    • (2007) J. Hydrol. Eng. , vol.12 , pp. 442-451
    • Tootle, G.1    Singh, A.2    Piechota, T.3    Farnham, I.4
  • 61
    • 0030126025 scopus 로고    scopus 로고
    • Probability distribution of annual maximum, mean and minimum streamflow values in the united states
    • Vogel R., Wilson I. Probability distribution of annual maximum, mean and minimum streamflow values in the united states. J. Hydrol. Eng. 1996, 1:69-76.
    • (1996) J. Hydrol. Eng. , vol.1 , pp. 69-76
    • Vogel, R.1    Wilson, I.2
  • 62
    • 0032813826 scopus 로고    scopus 로고
    • Regional regression models of annual streamflow for the united states
    • Vogel R., Wilson I., Daly C. Regional regression models of annual streamflow for the united states. J. Irrig. Drain. Eng. 1999, 125:148-157.
    • (1999) J. Irrig. Drain. Eng. , vol.125 , pp. 148-157
    • Vogel, R.1    Wilson, I.2    Daly, C.3
  • 64
    • 77958035437 scopus 로고    scopus 로고
    • Data-driven models for monthly streamflow time series prediction
    • Wu C.L., Chau K.W. Data-driven models for monthly streamflow time series prediction. Eng. Appl. Artif. Intell. 2010, 23:1350-1367.
    • (2010) Eng. Appl. Artif. Intell. , vol.23 , pp. 1350-1367
    • Wu, C.L.1    Chau, K.W.2
  • 65
    • 19744369661 scopus 로고    scopus 로고
    • Functional linear regression analysis for longitudinal data
    • Yao F., Muller H.-G., Wang J.-L. Functional linear regression analysis for longitudinal data. Ann. Statist. 2005, 2873-2903.
    • (2005) Ann. Statist. , pp. 2873-2903
    • Yao, F.1    Muller, H.-G.2    Wang, J.-L.3
  • 66
    • 77953342831 scopus 로고    scopus 로고
    • Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting
    • Yonaba H., Anctil F., Fortin V. Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting. J. Hydrol. Eng. 2010, 15:275-283.
    • (2010) J. Hydrol. Eng. , vol.15 , pp. 275-283
    • Yonaba, H.1    Anctil, F.2    Fortin, V.3
  • 67
    • 33746916489 scopus 로고    scopus 로고
    • Support vector regression for real-time flood stage forecasting
    • Yu P.-S., Chen S.-T., Chang I.F. Support vector regression for real-time flood stage forecasting. J. Hydrol. 2006, 328:704-716.
    • (2006) J. Hydrol. , vol.328 , pp. 704-716
    • Yu, P.-S.1    Chen, S.-T.2    Chang, I.F.3
  • 68
    • 0033019602 scopus 로고    scopus 로고
    • Short term streamflow forecasting using artificial neural networks
    • Zealand C.M., Burn D.H., Simonovic S.P. Short term streamflow forecasting using artificial neural networks. J. Hydrol. 1999, 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.