Identification of Water Quality Model Parameter Based on Finite Difference and Monte Carlo

DOI: 10.4236/jwarp.2013.512123   PDF   HTML     2,673 Downloads   4,258 Views   Citations


Identification results of water quality model parameter directly affect the accuracy of water quality numerical simulation. To overcome the difficulty of parameter identification caused by the measurement’s uncertainty, a new method which is the coupling of Finite Difference Method and Markov Chain Monte Carlo is developed to identify the parameters of water quality model in this paper. Taking a certain long distance open channel as an example, the effects to the results of parameters identification with different noise are discussed under steady and un-steady non-uniform flow scenarios. And also this proposed method is compared with finite difference method and Nelder Mead Simplex. The results show that it can give better results by the new method. It has good noise resistance and provides a new way to identify water quality model parameters.

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D. Shao, H. Yang and B. Liu, "Identification of Water Quality Model Parameter Based on Finite Difference and Monte Carlo," Journal of Water Resource and Protection, Vol. 5 No. 12, 2013, pp. 1165-1169. doi: 10.4236/jwarp.2013.512123.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] L. Jijian, W. Xu, L. Chanyu and M. Chao, “Emergency Regulation for Sudden Water Pollution Accidents of Open Chenannel in Long Distance Water Transfer Project,” Journal of Tianjin University (Science and Technology), Vol. 46, No. 1, 2013, pp. 44-50.
[2] Y. Xiaohua, Y. Zhifeng and L. Jianqiang, “A New Method for Parameter Identification in Water Environment Model,” Advances in Water Science, Vol. 14, No. 5, 2003, pp. 554-557.
[3] L. Xiaodong, Y. Qi and X. Hongqin, “Advance in Inverse Problem of Environmental Hydraulics,” Advances in Water Science, Vol. 20, No. 6, 2009, pp. 885-893.
[4] J. Ferrer, M. A. Pérez-Martín, S. Jiménez, T. Estrela and J. Andreu, “GIS-Based Models for Water Quantity and Quality Assessment in the Júcar River Basin, Spain, including Climate Change Effects,” Science of the Total Environment, No. 440, 2012, pp. 43-59.
[5] X. Hongqin, Z. Chen, L. Xiaodong and G. Li, “Finite Difference Method-Simplex Method for Determination of Longitudinal Dispersion Coefficient in Natural River,” Journal of PLA University of Science and Technology (Natural Science Edition), Vol. 13, No. 2, 2012, pp. 214-218.
[6] A. Afshar, N. Shojaei, and M. Sagharjooghifarahani, “Multiobjective Calibration of Reservoir Water Quality Modeling Using Multiobjective Particle Swarm Optimization (MOPSO),” Water Resources Management, Vol. 27, No. 7, 2013, pp. 1931-1947.
[7] L. Mengkai, W. Chang De and F. Xxiaobo, “Analysis on the Hydraulic Response of Long Distance Canal Control System during Ice Period,” Transactions of the CSAE, Vol. 27, No. 2, 2011, pp. 20-27.
[8] Z. Song, L. Guohua, M. Xinwei, et al., “Identification of Parameters for Standard k-ε Turbulence Model Based on Bayesian Inference,” Journal of Sichuan University: Engineering Sciences, Vol. 42, No. 4, 2010, pp. 78-82.
[9] D. McCarthy, A. Deletic, V. Mitchell and C. Diaper, “Sensitivity Analysis of an Urban Stormwater Microorganism Model,” Water Science and Technology, Vol. 62, No. 6, 2010, pp. 1393-1400.
[10] M. A. Friedrichs, “A Data Assimilative Marine Ecosystem Model of the Central Equatorial Pacific: Numerical twin experiments,” Journal of Marine Research, Vol. 59, No. 6, 2001, pp. 859-894.
[11] K. K. Khatua and K. C. Patra, “Flow Distribution in Meandering Compound Channel,” ISH Journal of Hydraulic Engineering, Vol. 15, No. 3, 2009, pp. 11-26.

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