A mixture-density-network based approach for finding rating curves: Facing multi-modality and unbalanced data distribution

KSCE Journal of Civil Engineering

Volumn 14, Issue 2, 2010, Pages 243-250

  Yoo, Chulsang ^a   Park, Jooyoung ^a  

^a Korea University   (South Korea)

Author keywords

Hysteresis; Mixture density networks; Multi layer perceptrons; Multi modality; Neural networks; Rating curves; Scaled conjugate gradients algorithms

Indexed keywords

MIXTURE DENSITY; MULTI-LAYER PERCEPTRONS; MULTI-MODALITY; RATING CURVE; SCALED CONJUGATE GRADIENTS; SCALED CONJUGATE GRADIENTS ALGORITHMS;

CONJUGATE GRADIENT METHOD; CYBERNETICS; HYSTERESIS; MIXTURES; PATTERN RECOGNITION SYSTEMS;

NEURAL NETWORKS;

EID: 77649207385     PISSN: 12267988     EISSN: 19763808     Source Type: Journal    
DOI: 10.1007/s12205-010-0243-0     Document Type: Article

References (19)

1. Birkhead, A. L. and James, C. S. (1998). "Synthesis of rating curves from local stage and remote discharge monitoring using non-linear Muskingum routing." Journal of Hydrology, Vol. 205, No. 1-2, pp. 52-65.
2. Bishop, C. M. (1994). Mixture density networks, Neural Computing Research Group Report: NCRG/94/004, Aston University.
3. Bishop, C. M. (1995). Neural networks for pattern recognition, Oxford University Press.
4. Clarke, R. T. (1999). "Uncertainty in the estimation of mean annual flood due to rating-curve identification." Journal of Hydrology, Vol. 222, No. 1-4, pp. 185-190.
5. DeGagene, P. J., Douglas, G. G., Hudson, H. R., and Simonovic, S. P. (1996). "A decision support system for the analysis and use of stagedischarge rating curves." Journal of Hydrology, Vol. 184, No. 3-4, pp. 225-241.
6. Hangang Flood Control Center (2003). http://www. hrfco. go. kr/YearCD/2003/discharge/index. html.
7. Haykin, S. (1998). Neural networks: A comprehensive foundation, 2nd Edition, Prentice Hall.
8. ISO (1983). Measurement of liquid flow in open channels, ISO Standard Handbook 16, International Organization of Standards, Geneva.
9. Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E. (1991). "Adaptive mixtures of local experts." Neural Computations, Vol. 3, No. 1, pp. 79-87.
10. Moller, M. (1993). "A scaled conjugate gradient algorithm for fast supervised learning." Neural Networks, Vol. 6, No. 4, pp. 525-533.
11. Nabney, I. T. (2001). NETLAB: Algorithms for pattern recognition, Springer.
12. Perumal, M., Shrestha, K. B., and Chaube, U. C. (2004). "Reproduction of hysteresis in rating curve." Journal of Hydraulic Engineering, ASCE, Vol. 130, No. 9, pp. 870-878.
13. Petersen-Øverleir, A. (2004). "Accounting for heteroscedasticity in rating curve estimates." Journal of Hydrology, Vol. 292, No. 1-4, pp. 173-181.
14. Rantz, S. E. (1982). Measurement and computation of stream flow, Volume 2, Computation of Discharge, Paper #2175, US Geological Survey Water Supply.
15. Schittenkopf, C., Dorffner, G., and Dockner, E. J. (1998). Identifying stochastic processes with mixture density networks, Technical Report TR-98-16, Austrian Research Institute for Artificial Intelligence, Vienna.
16. Singh, V. P. (1992). Elementary hydrology, Prentice-Hall, Inc., Eagle Wood Cliffs, NJ, pp. 406-407.
17. Supharatid, S. (2003). "Application of a neutral network model in establishing a stage-discharge relationship for a tidal river." Hydrological Processes, Vol. 17, No. 15, pp. 3085-3099.
18. Tawfik, M., Ibrahim, A., and Fahmy, H. (1997). "Hysteresis sensitive neural network for modeling rating curves." Journal of Computing in Civil Engineering, ASCE, Vol. 11, No. 3, pp. 206-211.
19. Yu, B. (2000). "A systematic over-estimation of flows." Journal of Hydrology, Vol. 233, No. 1-4, pp. 258-262.