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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.


The dynamic water quality assessment is a challenging and critical issue in water resource management systems. To deal with this complex problem, a dynamic water assessment model based on multiagent technology is proposed, and an improved Q-learning algorithm is used in this paper. In the proposed Q-learning algorithm, a fuzzy membership function and a punishment mechanism are introduced to improve the learning speed of Q-learning algorithm. The dynamic water quality assessment for different regions and the prewarning of water pollution are achieved by using an interaction factor in the proposed approach. The proposed approach can deal with various situations, such as static and dynamic water quality assessment. The experimental results show that the water quality assessment based on the proposed approach is more accurate and efficient than the general methods.