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Study on Water Quality Evaluation Method Based on Rough Sets and Neural Networks

Author: LiWei
Tutor: ShiWeiRen
School: Chongqing University
Course: Systems Engineering
Keywords: Water Evaluation Rough sets Artificial Neural Networks Rough Neuron Networks
CLC: X824
Type: Master's thesis
Year: 2008
Downloads: 245
Quote: 2
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Abstract


The water resources pollution problem in the Three Gorges area of the Yangtze River has drawn great attention from the outside and inside. The water environment is complicated, it also contains many factors and redundancy data in its evaluation. Both set up correct evaluation model and decrease complication of the model are what we supposed.The work of this dissertation is derived from a research project named Three Gorges Dam Area Water Pollution and Counter. Through the analyses of the dam area data, we set up the water evaluation model in a combined method which contains rough set theory and artificial neural networks theory.Rough sets is a kind of mathematical tool that is based upon math’s conception methods, which can be used to select the right evaluation factors set without any preliminary expert knowledge. Artificial neural networks have been applied in water evaluation area successfully because of its abilities of self-learning, self-organization, fault-tolerant and nonlinear- approximation. There are several combination strategies based on rough sets and artificial neural networks, two of them are discussed in this dissertation, one is using rough set method to preprocess the data and the other is a rough neural networks which contains rough neuron. At last, we also discussed the evaluation model combined of above two combination strategies, rough set method is used to preprocess the data and rough neural networks is used to evaluate the water.The main purpose of the data preprocessing by using rough sets is clean the noisy data and reduce the evaluation factors. After preprocessing, the reduced data will be as the input of artificial neural networks, and then the evaluation model will be set up by using BP neural networks. The advantages of this combination strategy are not only clean the noisy data and decrease the probability of over-fitting in trained neural networks, but also reduce the training data which save the training time and improve the efficiency.In rough neural networks, each rough neuron denotes an upper and lower boundary of a pattern, and rough neurons provide a capability on analyzing rough data. It is applied to deal with the data whose input and output are interval number.In this dissertation, the two combination strategies are compared with each other in the experiment of Three Gorges Dam Area water data. The result is that the strategy which uses rough set as the data preprocessing unit and rough neurons networks as the evaluation model unit is better than the other strategies. In real evaluation system, using rough sets to preprocess the data can improve the efficiency and get the set of evaluation factors which are important or indispensable for the evaluation, and this also improve the comprehension of evaluation model. At last, we applied the method in the real system.

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CLC: > Environmental science, safety science > Environmental Quality Assessment and Environmental Monitoring > Analysis and Evaluation of Environmental Quality > Water Quality Assessment
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