Quality evaluation and its application to surface water ecosystem based on maximum flux principle

Abstract

Based on the maximum flux principle (MFP), a water quality evaluation model for surface water ecosystem is presented by using self-organization map (SOM) neural network simulation algorithm from the aspect of systematic structural evolution. This evaluation model is applied to the case of surface water ecosystem in Xindu District of Chengdu City in China. The values reflecting the water quality of five cross-sections of the system at different developing stages are obtained, with stable values of 1.438, 2.952, 1.869, 2.443 and 2.479, respectively. The simulation also indicates that the larger the value, the more serious the water pollution. Furthermore, a classification graph is given to reflect the evolution of structural pattern. The combination of MFP and SOM neural network reveals the formation of different structural patterns in the system during the interaction of internal components. It is shown that a dominant pattern is finally reserved, which starts from a variety of combination patterns for a time period. The results agree with those from traditional evaluation methods, which indicates that the proposed model has high accuracy. This model embodies the evolutionary dynamic mechanisms and characteristics of temporal and spatial changes, which helps to guide the prediction of water quality status of surface water ecosystem.

Keywords

evaluation model / maximum flux principle / surface water ecosystem / self-organization map

References

[1] Hao M., Hou Jingyan. Health evaluation of the surface water ecosystem in Shenyang[J]. Environmental Protection Science, 2007, 33(2): 73-75.

[2] Meakin P. Fractals, Scaling, and Growth Far from Equilibrium[M]. 2000, Cambridge: Cambridge University Press.

[3] Cross M. C., Houenberg P. C. Pattern formation outside of equilibrium[J]. Reviews of Modern Physics, 1993, 65(3): 851-1112.

[4] Chai L. H., Wen D. S. Hierarchical self-organization of complex system[J]. Chemical Research in Chinese Universities, 2004, 20(4): 440-445.

[5] Vos R. J., Hakvoort J. H. M., Jordans R. W. J., et al. Multiplatform optical monitoring of eutrophication in temporally and spatially variable lakes[J]. Science of the Total Environment, 2003, 312(1–3): 221-243.

[6] Filik I. C., Emiroglu O., Ilhan S., et al. Application of multivariate statistical techniques in the assessment of surface water quality in Uluabat Lake, Turkey[J]. Environmental Monitoring and Assessment, 2008, 144(1–3): 269-276.

[7] Zhou F., Liu Y., Guo H. C., et al. Application of multivariate statistical methods to water quality assessment of the watercourses in Northwestern New Territories, Hong Kong[J]. Environmental Monitoring and Assessment, 2007, 132(1–3): 1-13.

[8] Shrestha S., Kazama F. Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan[J]. Environmental Modeling and Software, 2007, 22(4): 464-475.

[9] Zhou F., Guo H. C., Liu L. Quantitative identification and source apportionment of anthropogenic heavy metals in marine sediment of Hong Kong[J]. Environmental Geology, 2007, 53(2): 295-305.

[10] Liu Y., Yang P. J., Hu C., et al. Water quality modeling for load reduction under uncertainty: A Bayesian approach[J]. Water Research, 2008, 42(13): 3305-3314.

[11] Chang H. J. Spatial analysis of water quality trends in the Han River basin, South Korea[J]. Water Research, 2008, 42(13): 3285-3304.

[12] Subhankar K., Mujumdar P. P. A two-phase grey fuzzy optimization approach for water quality management of a river system[J]. Advances in Water Resources, 2007, 30(5): 1218-1235.

[13] Kauffman S. A. The Origins of Order: Self-Organization and Selection in Evolution[M]. 1993, New York: Oxford University Press.

[14] Annan J. D. Modeling under uncertainty: Monte Carlo methods for temporally varying parameters[J]. Ecological Modeling, 2001, 136(3): 297-302.

[15] Zhou J., Chai Lihe. Non-equilibrium statistical mechanics and applications based on new generalized variational principle[J]. Science and Technology Review, 2007, 25(10): 23-29.

[16] Chai L. H., Shoji M. Self-organization and self-similarity in boiling systems[J]. ASME Journal of Heat Transfer, 2002, 124(3): 507-515.

[17] Chai L. H. A theoretical analysis of bubble interaction in boiling systems[J]. International Journal of Thermal Sciences, 2004, 43(1): 1067-1073.

[18] Chen L. M., Chai L. H. A theoretical analysis on self-organized formation of microbial biofilms[J]. Physica A: Statistical Physics and Its Applications, 2006, 370(2): 793-807.

[19] Haken H. Information and Self-Organization[M]. 2000 2nd Edition Berlin: Springer.

[20] Fei-si ResearchDevelopment Center of Technological Product. Neural Network Theory and Matlab 7 Application[M]. 2005, Beijing: Publishing House of Electronics Industry.

[21] State Environmental Protection Agency. Surface Water Environment Quality Standard GB3838-2002[S]. 2003, Beijing: China Environmental Science Press.

[22] Zhou X. Q. Optimization and Application of Water Quality Assessment Model Based on Genetic Algorithms and Artificial Neural Networks [D]. 2008, Sichuan: College of Geography and Resource Science, Sichuan Normal University.