A Hybrid Rough Set Model for Water Quality Analysis

• Main Authors: Lee, Fong Chuan, 李鳳娟
• Other Authors: Pai, Ping-Feng
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/11032240595813530533

碩士 === 國立暨南國際大學 === 資訊管理學系 === 97 === Water quality analyses have started to put many efforts these days. In our everyday life, clean, fresh water for drinking, cooking, washing, sewage disposal, and agriculture is vital to healthy human life. Rough set theory (RST) is a novel technique in data mining and has been successfully employed in many fields. However, the application of RST has not been widely investigated in water quality analysis. Furthermore, the generation of reducts for RST models is very time-consuming when the problem size increases. Therefore, the aim of this investigation is to develop a hybrid model combining dimensionality reduction method, statistical method for attribute extraction and RST to analyze relation between water qualities and environmental factors in Taiwan. This study used environmental condition factors and the degree of water pollution to examine the feasibility of the proposed model. Empirical results indicated that the model combined with multinomial logistic regression and RST could analyze the water quality efficiently and accurately, and provide decision rules for staff of water quality management. Thus, the proposed model is a promising and helpful scheme in analyzing water quality.