Modelling algal blooms in Lake Dianchi, China using neural networks

Fresenius Environmental Bulletin

Volumn 16, Issue 7, 2007, Pages 798-803

  Li, Hongbin ^a   Hou, Guoxiang ^a,b   Song, Lirong ^b   Liu, Yongding ^b  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b INSTITUTE OF HYDROBIOLOGY   (China)

Author keywords

Algal bloom; Algal dynamics; Neural network; pH; Sensitivity analysis

Indexed keywords

ALGAL BLOOM; ARTIFICIAL NEURAL NETWORK; BIOMASS; MODELING; PH; SENSITIVITY ANALYSIS; WATER QUALITY;

ASIA; CHINA; DIANCHI LAKE; EURASIA; FAR EAST; YUNNAN;

ALGAE; ANABAENA; ANABAENA SP.; PEDIASTRUM;

EID: 34547764232     PISSN: 10184619     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (24)

1. Reynolds, C.S. (1984) The Ecology of Freshwater Phytoplankton. Publisher: Cambridge University Press, Cambridge, 384.
2. Whitehead, P.G. and Hornberger, G.M. (1984) Modelling algal behaviour in the River Thames. Wat. Res., 18(8), 945-953.
3. Recknagel, F. (1994) Hybrid expert system DELAQUA - a toolkit for water quality control of lakes and reservoirs. Ecol. Modelling, 71, 17-36.
4. Yabunaka, K., Hosomi, M. and Murakami, A. (1997) Novel application of a back-propagation artificial neural network model formulated to predict algal bloom. Wat. Sci. Tech., 36(5), 89-97.
5. Recknagel, F. (1997) ANNA - Artificial neural network model for predicting species abundance and succession of blue-green algae. Hydrobiologia, 349, 47-57.
6. Maier, H.R., and Dandy, G.C. (2001) Neural Network Based Modelling of Environmental Variables: A Systematic Approach. Mathematical and Computer Modelling, 33, 669-682.
7. Wei, B., Norio, S. and Takaaki, M. (2001) Use of artificial neural network in the prediction of algal blooms. Water Resource, 35(8), 2022-2028.
8. Hou G.X., Song L.R., Liu J.T., Xiao B.D., and Liu Y.D. (2004) Modeling of cyanobacterial blooms in hypereutrophic Lake Dianchi, China. Journal of Freshwater Ecology, 9(4), 623-629.
9. Liu, Y., Han, M., Liang, Z., and Lin, Y. (1995) Influence of light intensity, temperature and nutrients on the growth of Microcystis in water of Lake Dianchi. Res. Environ. Sci., 8, 7-11.
10. nd edition, Meteorological Press, Beijing.
11. Ma, X.X., He, X.J., Zhao, D.Q., and Wang X.Y. (2002) Influence of B-P networks hidden layer on water quality evaluation result. International Journal Hydroelectric Energy, 20, 16-18.
12. Xiong, L.H., Guo, S.L., and Wang, Y. (2002) Study and application of artificial neural network in real time flood forecasting. International Journal Hydroelectric Energy, 20, 28-31.
13. Xu, S.G. and Wang, J. (2002) Fuzzy optimal decision for structure of feedforward neural networks and its application in run-off forecast. International Journal Hydroelectric Energy, 20, 35-37.
14. Tzafestas, S.G., Dalianis, P.J., and Anthopoulos, G. (1996) On the overtraining phenomenon of backpropagation neural networks. Mathematics and Computers in Simulation, 40, 507-521.
15. Amari, S.-I., Murata, N.K.-R., Finke, M., and Yang, H.H. (1997) Asymptotic statistical theory of overtraining and cross-validation. IEEE Trans. on Neural Networks, 8 (5), 985-996.
16. Rivals, I. and Personnaz, L. (1999) On cross validation for model selection. Neural Computation, 11, 863-870.
17. Girosi, F., Jones, M. and Poggio, T. (1995) Regularization theory and neural networks architectures. Neural Computation, 7, 219-269.
18. Chen, D.D. and Hagan, M.T. (1999) Optimal use of regularization and cross validation in neural network modeling. Proceedings of the 1999 International Joint Conference on Neural Networks, 2, 1275-1280.
19. Garson, G.D. (1991) Interpreting neural network connection weights. Artif. Intel. Expert., 6, 47-51.
20. Goh, A.T.C. (1995) Back-propagation neural networks for modeling complex systems. Artif. Intel. Eng., 9, 143-151.
21. Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., and Aulagner, S. (1996) Application of neural networks to modeling non-linear relationships in ecology. Ecol. Model., 90, 39-52.
22. Siguier, I. (1997) Some artificial neural network applications to greenhouse environmental control. Comput. Electron. Agric., 18, 167-186.
23. Dimopoulos, Y., Chronopoulos, J., Chronopoulou, S.A., and Lek, S. (1999) Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city. Ecol. Model., 120, 157-165.
24. Disorntetiwat, P. (2001) Global stock index forecasting using multiple generalized regression neural networks with a gating network. Ph.D dissertation, University of Missouri-Rolla, Rolla.