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Volumn 17, Issue 1, 2013, Pages 253-267

Echo state networks as an alternative to traditional artificial neural networks in rainfall-runoff modelling

(1)  De Vos, N J a  

a NONE   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

FEED-FORWARD NETWORK; PERFORMANCE MEASURE; RAINFALL-RUNOFF MODELLING; RAINFALL-RUNOFF MODELS; RECURRENT ARTIFICIAL NEURAL NETWORKS; RELEVANT INFORMATIONS; STREAMFLOW FORECAST; TRAINING PROCEDURES;

EID: 84879033086     PISSN: 10275606     EISSN: 16077938     Source Type: Journal    
DOI: 10.5194/hess-17-253-2013     Document Type: Article
Times cited : (16)

References (54)
  • 1
    • 0034254196 scopus 로고    scopus 로고
    • Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments
    • Abrahart, R. J. and See, L. M.: Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments, Hydrol. Process., 14, 2157-2172, 2000.
    • (2000) Hydrol. Process , vol.14 , pp. 2157-2172
    • Abrahart, R.J.1    See, L.M.2
  • 2
    • 34249794005 scopus 로고    scopus 로고
    • Timing error correction procedure applied to neural network rainfall-runoff modelling
    • DOI 10.1623/hysj.52.3.414
    • Abrahart, R. J., Heppenstall, A. J. and See, L. M.: Timing error correction procedure applied to neural network rainfall-runoff modelling, Hydrol. Sci. J., 52, 414-431, doi:10.1623/hysj.52.3.414, 2007. (Pubitemid 46851531)
    • (2007) Hydrological Sciences Journal , vol.52 , Issue.3 , pp. 414-431
    • Abrahart, R.J.1    Heppenstall, A.J.2    See, L.M.3
  • 4
    • 84856404995 scopus 로고    scopus 로고
    • Discussion of "reservoir Computing approach to Great Lakes water level forecasting" by P. Coulibaly
    • Abrahart, R. J., Mount, N. J. and Shamseldin, A. Y.: Discussion of "Reservoir Computing approach to Great Lakes water level forecasting" by P. Coulibaly [J. Hydrol. 381 (2010) 76-88].
    • (2010) J. Hydrol. , vol.381 , pp. 76-88
    • Abrahart, R.J.1    Mount, N.J.2    Shamseldin, A.Y.3
  • 5
    • 84856404995 scopus 로고    scopus 로고
    • doi:10.1016/j.jhydrol.2011.10.006
    • J. Hydrol., 422-423, 76-80, doi:10.1016/j.jhydrol.2011.10.006, 2012b.
    • (2012) J. Hydrol , vol.422-423 , pp. 76-80
  • 6
    • 1142294041 scopus 로고    scopus 로고
    • A soil moisture index as an auxiliary ANN input for stream flow forecasting
    • DOI 10.1016/j.jhydrol.2003.09.006
    • Anctil, F., Michel, C., Perrin, C., and Andreassian, V.: A soil moisture index as an auxiliary ANN input for stream flow forecasting, J. Hydrol., 286, 155-167, 2004. (Pubitemid 38203954)
    • (2004) Journal of Hydrology , vol.286 , Issue.1-4 , pp. 155-167
    • Anctil, F.1    Michel, C.2    Perrin, C.3    Andreassian, V.4
  • 7
    • 0034186923 scopus 로고    scopus 로고
    • New results on recurrent network training: Unifying the algorithms and accelerating convergence
    • Atiya, A. F. and Parlos, A. G.: New results on recurrent network training: unifying the algorithms and accelerating convergence, IEEE T. Neural Netw., 11, 697-709, 2000.
    • (2000) IEEE T. Neural Netw , vol.11 , pp. 697-709
    • Atiya, A.F.1    Parlos, A.G.2
  • 8
    • 0034875916 scopus 로고    scopus 로고
    • A self-organizing NARX network and its application to prediction of chaotic time series
    • Barreto, G. de A. and Aráujo, A. F. R.: A self-organizing NARX network and its application to prediction of chaotic time series, in: Proceedings of the IEEE Intl. Joint Conference on Neural Networks, vol. 3, Washington D.C., USA, 2144-2149, 2001. (Pubitemid 32805231)
    • (2001) Proceedings of the International Joint Conference on Neural Networks , vol.3 , pp. 2144-2149
    • Barreto, G.A.1    Araujo, A.F.R.2
  • 9
    • 0028392483 scopus 로고
    • Learning long-term dependencies with gradient-descent is difficult
    • Bengio, Y., Simard, P., and Frasconi, P.: Learning long-term dependencies with gradient-descent is difficult, IEEE T. Neural Netw., 5, 157-166, 1994.
    • (1994) IEEE T. Neural Netw , vol.5 , pp. 157-166
    • Bengio, Y.1    Simard, P.2    Frasconi, P.3
  • 12
    • 33646536035 scopus 로고    scopus 로고
    • A tighter bound for the echo state property
    • Buehner, M. and Young, P.: A tighter bound for the echo state property, Neural Netw., 17, 820-824, 2006.
    • (2006) Neural Netw , vol.17 , pp. 820-824
    • Buehner, M.1    Young, P.2
  • 13
    • 0001632928 scopus 로고
    • The NWS river forecast system-catchment modeling
    • edited by: Singh V. P., Water Resources Publications, Colorado
    • Burnash, R. J. C.: The NWS river forecast system-catchment modeling, in: Computer Models of Watershed Hydrology, edited by: Singh, V. P., Water Resources Publications, Colorado, 311-366, 1995.
    • (1995) Computer Models of Watershed Hydrology , pp. 311-366
    • Burnash, R.J.C.1
  • 14
    • 0032688155 scopus 로고    scopus 로고
    • River flood forecasting with a neural network model
    • Campolo, M., Andreussi, P., and Soldati, A.: River flood forecasting with a neural network model, Water Resour. Res., 35, 1191-1197, 1999.
    • (1999) Water Resour. Res , vol.35 , pp. 1191-1197
    • Campolo, M.1    Andreussi, P.2    Soldati, A.3
  • 15
    • 0036719845 scopus 로고    scopus 로고
    • Real-time recurrent learning network for stream-flow forecasting
    • Chang, F. J., Chiang, L. C., and Huang, H. L.: Real-time recurrent learning network for stream-flow forecasting, Hydrol. Process., 16, 2577-2588, 2002.
    • (2002) Hydrol. Process , vol.16 , pp. 2577-2588
    • Chang, F.J.1    Chiang, L.C.2    Huang, H.L.3
  • 16
    • 1842426595 scopus 로고    scopus 로고
    • Comparison of static-feedforward and dynamic-feedback neural networks for rainfall-runoff modeling
    • Chiang, Y. M., Chiang, L. C., and Chang, F. J.: Comparison of static-feedforward and dynamic-feedback neural networks for rainfall-runoff modeling, J. Hydrol., 290, 297-311, 2004.
    • (2004) J. Hydrol , vol.290 , pp. 297-311
    • Chiang, Y.M.1    Chiang, L.C.2    Chang, F.J.3
  • 17
    • 73649127943 scopus 로고    scopus 로고
    • Reservoir computing approach to Great Lakes water level forecasting
    • Coulibaly, P.: Reservoir computing approach to Great Lakes water level forecasting, J. Hydrol., 381, 76-88, 2010.
    • (2010) J. Hydrol , vol.381 , pp. 76-88
    • Coulibaly, P.1
  • 18
    • 0034298548 scopus 로고    scopus 로고
    • A recurrent neural networks approach using indices of low-frequency climatic variability to forecast regional annual runoff
    • Coulibaly, P., Anctil, F., Rasmussen, P., and Bobee, B.: A recurrent neural networks approach using indices of low-frequency climatic variability to forecast regional annual runoff, Hydrol. Process., 14, 2755-2777, 2000.
    • (2000) Hydrol. Process , vol.14 , pp. 2755-2777
    • Coulibaly, P.1    Anctil, F.2    Rasmussen, P.3    Bobee, B.4
  • 19
    • 33644495279 scopus 로고    scopus 로고
    • Of data and models
    • Cunge, J. A.: Of data and models, J. Hydroinform., 5, 75-98, 2003.
    • (2003) J. Hydroinform , vol.5 , pp. 75-98
    • Cunge, J.A.1
  • 20
    • 23744444467 scopus 로고    scopus 로고
    • Constraints of artificial neural networks for rainfall-runoff modelling: Trade-offs in hydrological state representation and model evaluation
    • de Vos, N. J. and Rientjes, T. H. M.: Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation, Hydrol. Earth Syst. Sci., 9, 111-126, doi:10.5194/hess-9-111-2005, 2005. (Pubitemid 41122744)
    • (2005) Hydrology and Earth System Sciences , vol.9 , Issue.1-2 , pp. 111-126
    • De Vos, N.J.1    Rientjes, T.H.M.2
  • 21
    • 53849110109 scopus 로고    scopus 로고
    • Correction of timing errors of artificial neural network rainfall-runoff models
    • edited by: Abrahart, R. J., See, L. M., and Solomatine D. P., Water Science and Technology Library, Springer
    • de Vos, N. J. and Rientjes, T. H. M.: Correction of timing errors of artificial neural network rainfall-runoff models, in: Practical Hydroinformatics, edited by: Abrahart, R. J., See, L. M., and Solomatine, D. P., Water Science and Technology Library, Springer, 2008a.
    • (2008) Practical Hydroinformatics
    • De Vos, N.J.1    Rientjes, T.H.M.2
  • 22
    • 53849113979 scopus 로고    scopus 로고
    • Multi-objective training of artificial neural networks for rainfall-runoff modeling
    • doi:10.1029/2007WR006734
    • de Vos, N. J. and Rientjes, T. H. M.: Multi-objective training of artificial neural networks for rainfall-runoff modeling,Water Resour. Res., 44, W08434, doi:10.1029/2007WR006734, 2008b.
    • (2008) Water Resour. Res , vol.44
    • De Vos, N.J.1    Rientjes, T.H.M.2
  • 23
    • 0002223082 scopus 로고
    • Bifurcations in the learning of recurrent neural networks
    • San Diego, CA, USA
    • Doya, K.: Bifurcations in the learning of recurrent neural networks, in: Proc. IEEE Int. Symposium on Circuits and Systems, vol. 6, San Diego, CA, USA, 2777-2780, 1992.
    • (1992) Proc. IEEE Int. Symposium on Circuits and Systems , vol.6 , pp. 2777-2780
    • Doya, K.1
  • 25
    • 26444565569 scopus 로고
    • Finding structure in time
    • Elman, J. L.: Finding structure in time, Cognitive Sci., 14, 179-211, 1990.
    • (1990) Cognitive Sci , vol.14 , pp. 179-211
    • Elman, J.L.1
  • 26
    • 0028543366 scopus 로고
    • Training feedforward networks with the Marquardt algorithm
    • Hagan, M. T. and Menhaj, M. B.: Training feedforward networks with the Marquardt algorithm, IEEE T. Neural Netw., 5, 989-993, 1994.
    • (1994) IEEE T. Neural Netw. , vol.5 , pp. 989-993
    • Hagan, M.T.1    Menhaj, M.B.2
  • 30
    • 0029413797 scopus 로고
    • Artificial neural network modeling of the rainfall-runoff process
    • DOI 10.1029/95WR01955
    • Hsu, K.-L., Gupta, H. V., and Sorooshian, S.: Artificial neural network modeling of the rainfall-runoff process,Water Resour. Res., 31, 2517-2530, 1995. (Pubitemid 26475080)
    • (1995) Water Resources Research , vol.31 , Issue.10 , pp. 2517-2530
    • Kuo-Lin Hsu1    Gupta, H.V.2    Sorooshian, S.3
  • 32
    • 1842436050 scopus 로고    scopus 로고
    • The echo state approach to analysing and training recurrent neural networks
    • German National Research Center for Information Technology, St. Augustin, Germany
    • Jaeger, H.: The echo state approach to analysing and training recurrent neural networks, Tech. Report GMD Report 148, German National Research Center for Information Technology, St. Augustin, Germany, 2001.
    • (2001) Tech. Report GMD Report 148
    • Jaeger, H.1
  • 33
    • 33749833931 scopus 로고    scopus 로고
    • A tutorial on training recurrent neural networks covering BPPT RTRL EKF and the "echo state network" approach
    • German National Research Center for Information Technology, St. Augustin, Germany
    • Jaeger, H.: A tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the "echo state network" approach, Tech. Report GMD Report 159, German National Research Center for Information Technology, St. Augustin, Germany, 2002.
    • (2002) Tech. Report GMD Report 159
    • Jaeger, H.1
  • 34
    • 1842421269 scopus 로고    scopus 로고
    • Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
    • DOI 10.1126/science.1091277
    • Jaeger, H. and Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication, Science, 304, 78-80, 2004. (Pubitemid 38455427)
    • (2004) Science , vol.304 , Issue.5667 , pp. 78-80
    • Jaeger, H.1    Haas, H.2
  • 35
    • 2442639370 scopus 로고    scopus 로고
    • Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques
    • doi:10.1029/2003WR002355
    • Jain, A. and Srinivasulu, S.: Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques, Water Resour. Res., 40, W04302, doi:10.1029/2003WR002355, 2004.
    • (2004) Water Resour. Res , vol.40
    • Jain, A.1    Srinivasulu, S.2
  • 36
    • 0027601884 scopus 로고
    • ANFIS: Adaptive-network-based fuzzy inference systems
    • Jang, J. S., R.: ANFIS: Adaptive-network-based fuzzy inference systems, IEEE T. Syst. Man. Cyb., 23, 665-685, 1993.
    • (1993) IEEE T. Syst. Man. Cyb , vol.23 , pp. 665-685
    • Jang, J.S.R.1
  • 37
    • 0031581519 scopus 로고    scopus 로고
    • Development and test of the distributed HBV-96 hydrological model
    • DOI 10.1016/S0022-1694(97)00041-3, PII S0022169497000413
    • Lindstrom, G., Johansson, B., Persson, M., Gardelin, M., and Bergstrom, S.: Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272-288, 1997. (Pubitemid 28037764)
    • (1997) Journal of Hydrology , vol.201 , Issue.1-4 , pp. 272-288
    • Lindstrom, G.1    Johansson, B.2    Persson, M.3    Gardelin, M.4    Bergstrom, S.5
  • 38
    • 68649096445 scopus 로고    scopus 로고
    • Echo state networks with trained feedbacks
    • Jacobs University Bremen
    • Lukosevicius, M.: Echo state networks with trained feedbacks, Tech. Report No. 4, Jacobs University Bremen, 2007.
    • (2007) Tech. Report No. 4
    • Lukosevicius, M.1
  • 39
    • 68649088777 scopus 로고    scopus 로고
    • Reservoir computing approaches to recurrent neural network training
    • Lukosevicius, M. and Jaeger, H.: Reservoir computing approaches to recurrent neural network training, Comput. Sci. Rev., 3, 127-149, 2009.
    • (2009) Comput. Sci. Rev , vol.3 , pp. 127-149
    • Lukosevicius, M.1    Jaeger, H.2
  • 40
    • 0036834701 scopus 로고    scopus 로고
    • Real-time computing without stable states: A new framework for neural computation based on perturbations
    • Maass,W., Natschl̈ager, T., and Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations, Neural Comput., 14, 2531-2560, 2002.
    • (2002) Neural Comput , vol.14 , pp. 2531-2560
    • Maass, W.1    Natschlager, T.2    Markram, H.3
  • 41
    • 0027205884 scopus 로고
    • A scaled conjugate gradient algorithm for fast supervised learning
    • Møller, M. F.: A scaled conjugate gradient algorithm for fast supervised learning, Neural Netw., 6, 525-533, 1993.
    • (1993) Neural Netw , vol.6 , pp. 525-533
    • Møller, M.F.1
  • 42
    • 0028401031 scopus 로고
    • Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
    • Puskorius, G. V. and Feldkamp, L. A.: Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks, IEEE T. Neural Netw., 5, 279-297, 1994.
    • (1994) IEEE T. Neural Netw , vol.5 , pp. 279-297
    • Puskorius, G.V.1    Feldkamp, L.A.2
  • 44
    • 0032207527 scopus 로고    scopus 로고
    • Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks
    • IEEE
    • Saad, E. W., Prokhorov, D. V., and Wunsch, I.: Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks, IEEE T. Neural Netw., 9, 1456-1470, 1998.
    • (1998) Neural Netw. , vol.9 , pp. 1456-1470
    • Saad, E.W.1    Prokhorov, D.V.2    Wunsch, I.3
  • 45
    • 84879050581 scopus 로고    scopus 로고
    • A RBF network trained by the SONARX model and applied to obtain the operation policies of the hydropower systems
    • Brazil
    • Sacchi, R., Carneiro, A. A. F. M., and Aráujo, A. F. R.: A RBF network trained by the SONARX model and applied to obtain the operation policies of the hydropower systems, in: Brazilian Symposium on Neural Networks-SBRN, Brazil, 2004.
    • (2004) Brazilian Symposium on Neural Networks-SBRN
    • Sacchi, R.1    Carneiro, A.A.F.M.2    Araujo, A.F.R.3
  • 47
    • 0342506462 scopus 로고    scopus 로고
    • Application of a neural network technique to rainfall-runoff modelling
    • DOI 10.1016/S0022-1694(96)03330-6, PII S0022169496033306
    • Shamseldin, A. Y.: Application of a neural network technique to rainfall-runoff modelling, J. Hydrol., 199, 272-294, 1997. (Pubitemid 27492871)
    • (1997) Journal of Hydrology , vol.199 , Issue.3-4 , pp. 272-294
    • Shamseldin, A.Y.1
  • 48
    • 10944225085 scopus 로고    scopus 로고
    • Backpropagation-decorrelation: Recurrent Learning with O(N) Complexity
    • Budapest, Hungary
    • Steil, J. J.: Backpropagation-decorrelation: recurrent learning with O(N) complexity, in: Proceedings of the IEEE Intl. Joint Conference on Neural Networks, vol. 2, Budapest, Hungary, 843-848, 2004.
    • (2004) Proceedings of the IEEE Intl. Joint Conference on Neural Networks , vol.2 , pp. 843-848
    • Steil, J.J.1
  • 49
    • 0000647608 scopus 로고    scopus 로고
    • Extended Kalman filter-based pruning method for recurrent neural networks
    • Sum, J., Chan, L., Leung, C., and Young, G.: Extended Kalman filter-based pruning method for recurrent neural networks, Neural Comput., 10, 1481-1506, 1998.
    • (1998) Neural Comput , vol.10 , pp. 1481-1506
    • Sum, J.1    Chan, L.2    Leung, C.3    Young, G.4
  • 50
    • 34249815487 scopus 로고    scopus 로고
    • An experimental unification of reservoir computing methods
    • DOI 10.1016/j.neunet.2007.04.003, PII S089360800700038X, Echo State Networks and Liquid State Machines
    • Verstraeten, D., Schrauwen, B., D'Haene, M., and Stroobandt, D.: An experimental unification of reservoir computing methods, Neural Netw., 20, 391-403, 2007. (Pubitemid 46856109)
    • (2007) Neural Networks , vol.20 , Issue.3 , pp. 391-403
    • Verstraeten, D.1    Schrauwen, B.2    D'Haene, M.3    Stroobandt, D.4
  • 51
    • 0025503558 scopus 로고
    • Backpropagation through time: What it does and how to do it
    • Werbos, P. J.: Backpropagation through time: what it does and how to do it, Proc. IEEE, 78, 1550-1560, 1990.
    • (1990) Proc IEEE , vol.78 , pp. 1550-1560
    • Werbos, P.J.1
  • 52
    • 0001202594 scopus 로고
    • A learning algorithm for continually running fully recurrent neural networks
    • Williams, R. J. and Zipser, D.: A learning algorithm for continually running fully recurrent neural networks, Neural Comput., 1, 270-280, 1989.
    • (1989) Neural Comput , vol.1 , pp. 270-280
    • Williams, R.J.1    Zipser, D.2
  • 54
    • 77953342831 scopus 로고    scopus 로고
    • Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting
    • Yonaba, H., Anctil, F., and Fortin, V.: Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting, J. Hydrol. Eng., 15, 275-283, 2010.
    • (2010) J. Hydrol. Eng , vol.15 , pp. 275-283
    • Yonaba, H.1    Anctil, F.2    Fortin, V.3


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