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Mathematical Problems in Engineering
Volume 2013, Article ID 812032, 7 pages
Research Article

A Multiagent Dynamic Assessment Approach for Water Quality Based on Improved Q-Learning Algorithm

1College of IOT Engineering, Hohai University, Changzhou 213022, China
2College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
3Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Shanghai 200135, China

Received 30 March 2013; Revised 17 April 2013; Accepted 18 April 2013

Academic Editor: Guanghui Wen

Copyright © 2013 Jianjun Ni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Q.-G. Wang, X.-H. Zhao, W.-J. Wu, M.-S. Yang, Q. Ma, and K. Liu, “Advection-diffusion models establishment of water-pollution accident in middle and lower reaches of Hanjiang river,” Advances in Water Science, vol. 19, no. 4, pp. 500–504, 2008. View at Google Scholar
  2. P. Tang, L. Zhao, L. Ren, Z. Zhao, and Y. Yao, “Real time monitoring of surface water pollution using microwave system,” Journal of Electromagnetic Waves and Applications, vol. 22, no. 5-6, pp. 767–774, 2008. View at Google Scholar
  3. N. Pochai, “A numerical treatment of nondimensional form of water quality model in a nonuniform flow stream using Saulyev scheme,” Mathematical Problems in Engineering, vol. 2011, Article ID 491317, 15 pages, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  4. S. Rodgher, H. de Azevedo, C. R. Ferrari et al., “Evaluation of surface water quality in aquatic bodies under the influence of uranium mining (MG, Brazil),” Environmental Monitoring and Assessment, vol. 185, no. 3, pp. 2395–2406, 2013. View at Google Scholar
  5. L. KeGang and H. KePeng, “Dynamic extension evaluation of soil and water environmental quality in metal mine and its improvement measures,” Research Journal of Chemistry and Environment, vol. 16, no. 2, pp. 97–101, 2012. View at Google Scholar
  6. Q. Wu, C. Zhao, and Y. Zhang, “Landscape river water quality assessment by nemerow pollution index,” in Proceedings of the International Conference on Mechanic Automation and Control Engineering (MACE '10), pp. 2117–2120, Wuhan, China, June 2010.
  7. J. Shu, M. Hong, L. Liu, and Y. Chen, “A water quality monitoring method based on fuzzy comprehensive evaluation in wireless sensor networks,” Journal of Networks, vol. 7, no. 1, pp. 195–202, 2012. View at Google Scholar
  8. S. Ni and Y. Bai, “Application of BP neural network model in groundwater quality evaluation,” System Engineering Theory and Practice, vol. 20, no. 8, pp. 124–127, 2000. View at Google Scholar
  9. Z.-X. Xu, “Comprehensive water quality identification index for environmental quality assessment of surface water,” Journal of Tongji University, vol. 33, no. 4, pp. 482–488, 2005. View at Google Scholar
  10. Y. Yun, Z. Zou, W. Feng, and M. Ru, “Quantificational analysis on progress of river water quality in China,” Journal of Environmental Sciences, vol. 21, no. 6, pp. 770–773, 2009. View at Google Scholar
  11. S. Su, J. Zhi, L. Lou, F. Huang, X. Chen, and J. Wu, “Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques,” Physics and Chemistry of the Earth, vol. 36, no. 9–11, pp. 379–386, 2011. View at Google Scholar
  12. W. Sun and Z. Zeng, “City optimal allocation of water resources research based on sustainable development,” Advanced Materials Research, vol. 446–449, pp. 2703–2707, 2012. View at Google Scholar
  13. G.-H. Wei, F. Liu, and L. Ma, “Fuzzy optimization of water resources project scheme based on improved grey relation analysis,” in Proceedings of the 3rd International Conference on Computer Research and Development, vol. 4, pp. 333–336, Shanghai, China, 2011.
  14. Y. Cao, W. Yu, W. Ren, and G. Chen, “An overview of recent progress in the study of distributed multi-agent coordination,” IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 427–438, 2013. View at Google Scholar
  15. C. Li, F. Wang, X. Wei, and Z. Ma, “Solution method of optimal scheme set for water resources scheduling group decision-making based on multi-agent computation,” Intelligent Automation and Soft Computing, vol. 17, no. 7, supplement 1, pp. 871–883, 2011. View at Google Scholar
  16. G. Wen, Z. Duan, W. Yu, and G. Chen, “Consensus of multi-agent systems with nonlinear dynamics and sampled-data information: a delayed-input approach,” International Journal of Robust and Nonlinear Control, vol. 23, no. 6, pp. 602–619, 2013. View at Google Scholar
  17. F. Leon, “Emergent behaviors in social networks of adaptive agents,” Mathematical Problems in Engineering, vol. 2012, Article ID 857512, 19 pages, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  18. J. Wang, K. Gwebu, M. Shanker, and M. D. Troutt, “An application of agent-based simulation to knowledge sharing,” Decision Support Systems, vol. 46, no. 2, pp. 532–541, 2009. View at Google Scholar
  19. J. Ni, C. Zhang, and L. Ren, “An intelligent decision support system of lake water pollution control based on multi-agent model,” in Proceedings of the International Conference on Computational Intelligence and Security (CIS '09), vol. 1, pp. 217–221, Beijing, China, December 2009.
  20. J. Ni, M. Liu, J. Fei, and H. Ma, “Reinforcement learning based multi-agent cooperation for water price forecasting decision support system,” Information-An International Interdisciplinary Journal, vol. 15, no. 5, pp. 1889–1899, 2012. View at Google Scholar
  21. M.-L. Xu and W.-B. Xu, “Fuzzy Q-learning in continuous state and action space,” Journal of China Universities of Posts and Telecommunications, vol. 17, no. 4, pp. 100–109, 2010. View at Google Scholar
  22. S. Zheng, J. Han, X. Luo, and J. Jiang, “Research on cooperation and reinforcement learning algorithm in multi-agent systems,” Pattern Recognition and Artificial Intelligence, vol. 15, no. 4, pp. 453–457, 2002. View at Google Scholar
  23. A. Bonarini, A. Lazaric, F. Montrone, and M. Restelli, “Reinforcement distribution in fuzzy Q-learning,” Fuzzy Sets and Systems, vol. 160, no. 10, pp. 1420–1443, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  24. X. Xu, C. Liu, S. X. Yang, and D. Hu, “Hierarchical approximate policy iteration with binarytree state space decomposition,” IEEE Transactions on Neural Networks, vol. 22, no. 12, part 1, pp. 1863–1877, 2011. View at Google Scholar
  25. M. L. Littman, “Value-function reinforcement learning in Markov games,” Cognitive Systems Research, vol. 2, no. 1, pp. 55–66, 2001. View at Google Scholar
  26. X. Xu, D. Hu, and X. Lu, “Kernel-based least squares policy iteration for reinforcement learning,” IEEE Transactions on Neural Networks, vol. 18, no. 4, pp. 973–992, 2007. View at Google Scholar
  27. J. Wu, X. Xu, P. Zhang, and C. Liu, “A novel multi-agent reinforcement learning approach for job scheduling in Grid computing,” Future Generation Computer Systems, vol. 27, no. 5, pp. 430–439, 2011. View at Google Scholar
  28. K. Fujita and H. Matsuo, “Multiagent reinforcement learning with the partly high-dimensional state space,” Systems and Computers in Japan, vol. 37, no. 9, pp. 22–31, 2006. View at Google Scholar
  29. K.-D. Liu, Y.-J. Pang, and W.-G. Li, “Membership transforming algorithm in multi-index decision and its application,” Acta Automatica Sinica, vol. 35, no. 3, pp. 315–319, 2009. View at Google Scholar
  30. X.-H. Zhao, K.-K. Zhao, Q.-Q. Wang, and F.-Q. Ma, “Research and application of reinforcement learning based on constraint MDP in coal mine,” in Proceedings of the WRI World Congress on Computer Science and Information Engineering (CSIE '09), vol. 4, pp. 687–691, Los Angeles, Calif, USA, March 2009.
  31. V. Derhami, V. J. Majd, and M. N. Ahmadabadi, “Exploration and exploitation balance management in fuzzy reinforcement learning,” Fuzzy Sets and Systems, vol. 161, no. 4, pp. 578–595, 2010. View at Publisher · View at Google Scholar · View at MathSciNet