Water quality evaluation entails both randomness and fuzziness. Considering that water eutrophication evaluation involves many indices, different classifications and interval values, fuzzy variable sets theory was dev...Water quality evaluation entails both randomness and fuzziness. Considering that water eutrophication evaluation involves many indices, different classifications and interval values, fuzzy variable sets theory was developed to Lake Baiyangdian as a study case. Taking reference to eutrophication standard of Chinese lakes and local characteristic of Lake Baiyangdian, eutrophication degree of lake was divided into 8 levels. Total phosphorus, total nitrogen, and CODMn were selected as evaluation indices in this research. Based on the measured data, index feature value matrix of sample was built. Index weights were determined by means of pure threshold value method. Relative membership degree of each index to each classification was calculated with relative difference function model. Then the stability of feature value of classification corresponding was received by the comprehensive calculation with the relative membership degree and index weights. The results show that the proposed models are effective tools for generating a set of realistic and flexible optimal solutions for complicated water quality evaluation issues. It concluded that the model was reasonable and practical.展开更多
文摘Water quality evaluation entails both randomness and fuzziness. Considering that water eutrophication evaluation involves many indices, different classifications and interval values, fuzzy variable sets theory was developed to Lake Baiyangdian as a study case. Taking reference to eutrophication standard of Chinese lakes and local characteristic of Lake Baiyangdian, eutrophication degree of lake was divided into 8 levels. Total phosphorus, total nitrogen, and CODMn were selected as evaluation indices in this research. Based on the measured data, index feature value matrix of sample was built. Index weights were determined by means of pure threshold value method. Relative membership degree of each index to each classification was calculated with relative difference function model. Then the stability of feature value of classification corresponding was received by the comprehensive calculation with the relative membership degree and index weights. The results show that the proposed models are effective tools for generating a set of realistic and flexible optimal solutions for complicated water quality evaluation issues. It concluded that the model was reasonable and practical.
