A connection cloud model coupled with extenics for water eutrophication evaluation

The evaluation of water quality is challenging because it is involved with various uncertainty factors. A connection cloud model coupled with extenics, taking into account of randomness, fuzziness and incompatibility of evaluation indicators, was presented here to analyze the water quality. First, according to the classification standard, left and right half interval lengths of evaluation indicator were specified to assign the digital features of the connection cloud at various levels. Then, a matter element was built with the connection cloud model. Namely, connection clouds in finite intervals were simulated to analyze the certainty degree of measured indicator to each evaluation standard, the certainty degree of indicator was calculated, and the extension matrix was constructed based on connection cloud. Next, associated with the weight vector of indicators, the integrated certainty degree was calculated to determine the class of water quality. Finally, a case study and comparisons with other methods were performed to confirm the validity and reliability of the proposed model. The results show that this model can not only quantitatively describe certainty and uncertainty relationship between evaluation indicators and classification standard in a unified way, but also make the evaluation result more reasonable.