Estimation of ice thickness on lakes using artificial neural network ensembles

Journal of Hydrology

Volumn 383, Issue 3-4, 2010, Pages 330-340

  Zaier, I ^a   Shu, C ^a   Ouarda, T B M J ^a   Seidou, O ^b   Chebana, F ^a  

^a CENTRE EAU TERRE ENVIRONNEMENT   (Canada)

^b UNIVERSITY OF OTTAWA   (Canada)

Author keywords

Artificial neural networks; Bagging; Boosting; Ice thickness; Lake ice growth; Neural network ensemble

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BAGGING; ICE GROWTH; ICE THICKNESS; NEURAL NETWORK ENSEMBLES;

ESTIMATION; ICE; LAKES;

NEURAL NETWORKS;

ERROR ANALYSIS; ESTIMATION METHOD; EXPERIMENTAL STUDY; ICE THICKNESS; LAKE; MODEL TEST; PREDICTION; WATER QUALITY;

CANADA;

EID: 77549086197     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2010.01.006     Document Type: Article

References (68)

1. Ahmad, Z., Zhang, J., 2002. A comparison of different methods for combining multiple neural networks models. In: Proceedings of the 2002 World Congress on Computational Intelligence, pp. 12-117.
2. Agrafiotis D.K., Cedeno W., and Lobanov V.S. On the use of neural network ensembles in QSAR and QSPR. J. Chem. Inf. Comput. Sci. 42 (2002) 903-911
3. Bates J.M., and Granger C.W.J. The combination of forecasts. Oper. Res. Quart. 20 (1969) 451-468
4. Bishop C.M. Neural Networks for Pattern Recognition (1995), Oxford Univ. Press, New York
5. Breiman L. Bagging predictors. Mach. Learn. 24 (1996) 123-140
6. Breiman L. Stacked regressions. Mach. Learn. 24 (1996) 49-64
7. Cannon A.J., and Whitfield P.H. Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models. J. Hydrol. 259 (2002) 136-151
8. Cheng B., and Titterington D.M. Neural network a review from a statistical perspective. Stat. Sci. l (1994) 2-54
9. Chokmani K., Ouarda T.B.M.J., Hamilton S., Ghedira M.H., and Ginras H. Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques. J. Hydrol. 349 (2008) 383-396
10. Cybenko G. Approximation by superposition of as sigmoidal function. Math. Control Signals Syst. 2 (1989) 303-314
11. Demuth, H., Beale, M., Hagan, M., 2008. Matlab Neural Network Toolbox 6, User's Guide. The MathWorks Inc.
12. Dietterich T.G. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach. Learn. 40 2 (2000) 139-157
13. Drucker, H., 1997. Improving regressors using boosting techniques. In: Machine Learning: Proceedings of the Fourteenth International Conference, pp. 107-115.
14. Drucker H. Boosting using neural networks. In: Sharkey A.J.C. (Ed). Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems (1999), Springer-Verlag, New York 51-78
15. Duguay C.R., Flato G.M., Heffries M.O., Ménard P., Morris K., and Rouse W.R. Ice covers variability on shallow lakes at high altitudes: model simulation and observations. Hydrol. Process. 17 (2003) 3465-3483
16. Efron B. Computers and the theory of statistics: thinking the unthinkable. SIAM Rev. (1979) 21-460
17. Efron B., and Tibshirani R. An Introduction to the Bootstrap (1993), Chapman and Hall
18. English T.M. Stacked generalization and simulated evolution. Biosystems 39 1 (1996) 3-18
19. Flato G.M., and Brown R. Variability and climate sensitivity of land fast arctic sea ice. J. Geophys. Res. 101 (1996) 25767-25777
20. Freund Y., and Schapire R.E. Experiments with a new boosting algorithm. Proceedings of the Thirteenth International Conference on Machine Learning (1996), Morgan Kaufmann, Burlington, Mass 148-156
21. Funahashi K. On the approximate realization of continuous mappings by neural networks. Neural Networks 2 (1989) 183-192
22. Grover P.L., Burn D.H., and Cunderlik J.M. A comparison of index flood estimation procedures for ungauged catchments. Can. J. Civ. Eng. 29 5 (2002) 734-741
23. Hansen L., and Salamon P. Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12 (1990) 993-1001
24. Hornik K., Stinchcombe M., and White H. Multilayer feedforward neural networks are universal approximators. Neural Networks 2 (1989) 359-366
25. Hu M.Y., and Tsoukalas C. Explaining consumer choice through neural networks: the stacked generalization approach. Eur. J. Oper. Res. 146 3 (2003) 650-660
26. Jacobs R.A., Jordan M.I., Nowlan S.J., and Hinton G.E. Adaptive mixture of local experts. Neural Comput. 3 (1991) 79-87
27. Jordan M.I., and Jacobs R.A. Hierarchical mixture of experts and the EM algorithm. Neural Comput. 6 (1994) 181-214
28. Krogh A., and Vedelsby J. Neural network ensembles, cross-validation and active learning. In: Tesauro G., Touretzky D.S., and Leen T.K. (Eds). Advances in Neural Information Processing Systems vol. 7 (1995), The MIT Press, Cambridge, Massachusetts 231-238
29. Liang R.H., and Hsu Y. Scheduling of hydroelectric generation using artificial neural networks. IEE Proc., C 145 (1994) 452-458
30. Lock G.S.H. The Growth and Decay of Ice (1990), Cambridge Univ. Press, New York
31. MacKay D.J.C. A practical Bayesian framework for backpropagation networks. Neural Comput. 4 3 (1992) 448-472
32. Maier H.R., and Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Model. Softw. 15 (2000) 101-123
33. Matousek, V., 1984. Regularity of the freezing up of the water surface and heat exchange between water body and water surface. In: Int. Assoc. Hydraul. Eng. and Res. Hamburg, Germany.
34. Ménard P., Duguay C.R., Flato G.M., and Rouse W.R. Simulation of ice phenology on Great Slave Lake, Northwest Territories, Canada. Hydrol. Process. 16 (2002) 3691-3706
35. Ménard, P., Duguay, C.R., Flato, G.M., Rouse, W.R., 2002b. Simulation of ice phenology on a large lake in the Mackenzie River basin. In: Proceedings of the 59th Annual Meeting of Eastern Snow Conference, pp. 3-12.
36. Omstedt A. On supercooling and ice formation in turbulent seawater. J. Glaciol. 31 (1985) 272-280
37. Omstedt A. Modeling frazil ice and grease ice formation in the upper layers of the ocean. Cold Reg. Sci. Technol. 11 (1985) 87-98
38. Opitz D., and Maclin R. Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11 (1999) 169-198
39. Ouarda, T.B.M.J., Gingras, H., Hamilton, S., Ghedira, H., Bobée, B., 2003. Estimation of Streamflow Under Ice, Paper Presented at the 12th Workshop on the Hydraulics of Ice Covered Rivers, Committee on River Ice Processes and the Environment, Canadian Geophysical Union [CGU-HS], Edmonton, pp. 19-21.
40. Pandey G.R., and Nguyen V.-T.-V. A comparative study of regression based methods in regional flood frequency analysis. J. Hydrol. 225 (1999) 92-101
41. Perrone M.P., and Cooper L.N. When networks disagree: ensemble methods for hybrid neural networks. In: Mammone R.J. (Ed). Artificial Neural Networks for Speech and Vision (1993), Chapman and Hall, New York 126-142
42. Ripley B.D. Statistical aspect.6 of neural networks. In: Barndorff-Nielsen O.E., Jensen J.L., and Kendall W.S. (Eds). Networks and Chaos-Statistical and Probabilistic Aspects (1993), Chapman and Hell, London 40-123
43. Rumelhart D.E., and McClelland J.L. Parallel Distributed Processing: Explorations in the Microstructure of Cognition (1986), MIT Press (1), USA
44. Shamseldin A.Y. Application of neural network technique to rainfall-runoff modeling. J. Hydrol. 199 (1997) 272-294
45. Schapire R.E. The strength of weak learnability. Mach. Learn. 5 (1990) 197-227
46. Schulyakovskii, L.G., (Ed.), 1966. Manual of Ice-Formation Forecasting for Rivers and Inland Lakes. Isr. Prog. for Sci. Transl., Jerusalem.
47. Seidou O., Ouarda T.B.M.J., Bilodeau L., Hessami M., St-Hilaire A., and Bruneau P. Modelling ice growth on Canadian lakes using artificial neural networks. Water Resour. Res. (2006) 42
48. In: Sharkey A.J.C. (Ed). Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems (1999), Springer-Verlag, New York
49. Shen H.T., and Chiang L.A. Simulation of growth and decay of river ice cover. J. Hydraul. Eng. 110 (1984) 958-971
50. Shen, H.T., Ho, C.F., 1986. Two-dimensional Simulation of Ice Cover Formation in a Large River, Paper Presented at IAHR Ice Symposium. Int. Assoc. of Hydraul. Eng. And Res., Iowa City, Iowa.
51. Shen H.T., Shen H.H., and Tsai S.M. Dynamic transport of river ice. J. Hydraul. Res. 28 (1990) 659-671
52. Shen H.T., Wang D.S., and Lal A.M.W. Numerical simulation of river ice processes. J. Cold Reg. Eng. 107 (1995) 107-118
53. Shu C., and Burn D.H. Artificial neural network ensembles and their application in pooled flood frequency analysis. Water Resour. Res. 40 9 (2004)
54. Shu C., and Ouarda T.B.M.J. Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space. Water Resour. Res. 7 (2007) 43
55. Shu C., and Ouarda T.B.M.J. Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J. Hydrol. 349 1-2 (2008) 31-43
56. Solar Radiation Monitoring Laboratory, 2004. Solar Radiation Basics, Report, Univ. of Oreg., Corvallis. .
57. Sridhar D.V., Seagrave R.C., and Bartlett E.B. Process modelling using stacked neural networks. AIChE J. 42 (1996) 2529-2539
58. Svensson U., and Omstedt A. Simulation of supercooling and size distribution of frazil ice dynamics. Cold Reg. Sci. Technol. 22 (1994) 221-233
59. Svensson U., Billfalk L., and Hammar L. A mathematical model of border-ice formation in rivers. Cold Reg. Sci. Technol. 16 (1989) 179-189
60. Tangang F.T., Tang B., Monahan A.H., and Hsieh W.W. Forecasting ENSO events-a neural network-extended EOF approach. J. Climate 11 (1998) 29-41
61. Ting K.M., and Witten I.H. Issues in stackedgeneralization. J. Artif. Intell. Res. 10 (1999) 271-289
62. Werbos, P.J., 1974. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Thèse de PhD, Harvard University, Cambridge, États-Unis.
63. White H. Learning in artificial neural networks: a statistical perspective. Neural Comput. 1 (1989) 425-464
64. Witten I.H., and Frank E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (1999), Morgan Kaufmann, San Francisco
65. Wolpert D.H. Stacked generalization. Neural Networks 5 (1992) 241-259
66. Xiao R., and Chandrasekar V. Development of a neural network based algorithm for rainfall estimation from radar observation. IEEE Trans. Geosci. Remote Sens. 35 (1997) 160-171
67. Zhang J., Martin E.B., Morris A.J., and Kiparissides C. Inferential estimation of polymer quality using stacked neural networks. Comput. Chem. Eng. 21 (1997) s1025-s1030
68. Zhang S.P., Watanabe H., and Yamada R. Prediction of daily water demand by neural networks. In: Hipel K.W., et al. (Ed). Stochastic and Statistical Methods in Hydrology and Environmental Engineering vol. 3 (1994), Springer, New York 217-227