A Rough Set Based Model in Water Quality Analysis |
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Authors: | Ping-Feng Pai Fong-Chuan Lee |
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Affiliation: | (1) Key Laboratory of Water and Sediment Science, Ministry of Education, College of Water Sciences, Beijing Normal University, 100875 Beijing, China; |
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Abstract: | Due to pollution caused by the expansion of human activities and economic development, water quality has gradually deteriorated
in many areas of the world. Therefore, analysis of water quality becomes one of the most essential issues of modern civilization.
Integrated interdisciplinary modeling techniques, providing reliable, efficient, and accurate representation of the complex
phenomenon of water quality, have gained attention in recent years. With the ability to deal with both numeric and nominal
information, and express knowledge in a rule-based form, the Rough Set Theory (RST) has been successfully employed in many
fields. However, the application of RST has not been widely investigated in water quality analysis. The reducts generated
by RST models become very time-consuming as the size of the problem increases. Using multinomial logistics regression (MLR)
techniques to provide reducts of RST models, this investigation develops a hybrid Multinomial Logistic Regression and Rough
Set Theory (MLRRST) model to analyze relations between degrees of water pollution and environmental factors in Taiwan. Empirical
results indicate that the MLRRST model could analyze water qualities efficiently and accurately, and yield decision rules
for the staff of water quality management. Thus, the proposed model is a promising and helpful scheme in analyzing water quality. |
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