Fuzzy Integral Based Information Fusion for Water Quality Monitoring Using Remote Sensing Data
Huibin Wang, Tanghuai Fan, Aiye Shi, Fengchen Huang, Huimin Wang
.
DOI: 10.4236/ijcns.2010.39098   PDF    HTML     4,460 Downloads   8,479 Views   Citations

Abstract

To improve the monitoring precision of lake chlorophyll a (Chl-a), this paper presents a fusion method based on Choquet Fuzzy Integral (CFI) to estimate the Chl-a concentration. A group of BPNN models are designed. The output of multiple BPNN model is fused by the CFI. Meanwhile, to resolve the over-fitting problem caused by a small number of training sets, we design an algorithm that fully considers neighbor sampling information. A classification experiment of the Chl-a concentration of the Taihu Lake is conducted. The result shows that, the proposed approach is superior to the classification using a single neural network classifier, and the CFI fusion method has higher identification accuracy.

Share and Cite:

H. Wang, T. Fan, A. Shi, F. Huang and H. Wang, "Fuzzy Integral Based Information Fusion for Water Quality Monitoring Using Remote Sensing Data," International Journal of Communications, Network and System Sciences, Vol. 3 No. 9, 2010, pp. 737-744. doi: 10.4236/ijcns.2010.39098.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] P. E. Greeson, “Lake Eutrophication-A Natural Process,” JAWRA Journals of the American Water Resources Association, Vol. 5, No. 4, 2007, pp. 16-30.
[2] Q. Yin, C. Gong, D. Kuang, N. Zhou, Y. Hu, et al., “Method of Satelite Remote Sensing of Lake Water Quality and Its Applications,” Journals of Infrared and Millimeter Waves (in Chinese), Vol. 24, No. 3, 2005, pp. 198-202.
[3] G. Hanrahan, “Modelling of Pollutants in Complex Envi- ronmental Systems”, ILM Publications, St. Albans, 2009.
[4] R. O. Strobl, F. Forte and L. Pennetta, “Application of Artificial Neural Networks for Classifying Lake Eutro- phication Status,” Lakes & Reservoirs: Research & Management, Vol. 12, No. 1, 2007, pp. 15-25.
[5] L. Chen, “A Study of Applying Genetic Programming to Reservoir Trophic State Evaluation Using Remote Sensor Data,” International Journals of Remote Sensing, Vol. 24, No. 11, 2003, pp. 2265-2276.
[6] A. Shi, T. Fan,L. Xu and J. Zhou, “A Fuzzy Integral Model for Estimating Chlorophyll Concentrations in Tai Lake from Thematic Mapper Imagery,” Proceedings of IEEE International Conference on Information Acquisition, Shandong, China, 2006, pp. 1117-1121.
[7] B. Zhang and J. L. Liu, “Evaluation Method for Lake Eutro- phication Influence and Public Satisfaction,” Advances in Water Science, Vol. 20, No. 5, 2009, pp. 695-700.
[8] D. E. Rumelhart, J. L. McClelland and the PDP research group, “Parallel Distributed Processing: Explorations in the Microstructure of Cognition,” MIT Press, Cambridge, 1986.
[9] M. Sugeno, “Theory of Fuzzy Integrals and Its Applications”, Tokyo Institute of Technology Tokyo, Japan, 1974.
[10] J. H. Chiang, “Aggregating Membership Values by a Choquet-fuzzy-integral Based Operator,” Fuzzy Sets and Systems, Vol. 114, No. 3, 2000, pp. 367-375.
[11] S. Auephanwiriyakul, J. Keller and P. Gader, “Generalized Choquet Fuzzy Integral Fusion,” Information Fu- sion, Vol. 3, No. 1, 2002, pp. 69-85.
[12] L. Yang and Y. Gao, “Principle and Application of Fuzzy Mathematics,” South China University of Technology Press, Guangzhou, China, 2001.
[13] H. Duan, L. Yu, B. Zhang, D. Liu, K. Song and Z. Wang, “Hyperspectral Remotesensing of Chlorophyll-a in The Chagan Lake,” Environmental Science, Vol. 27, No. 3, 2006, pp. 503-507.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.