A cloud model based on hybrid similarity approach for water quality evaluation

EasyChair Preprint 1296

Abstract

Water quality assessment is an essential way to research water environment. Considered uncertainty of randomness and fuzziness in water quality assessment, a cloud model based on hybrid similarity approach (CHS) for water quality assessment was proposed in this paper. Structure Equation Model(SEM) and Entropy methods based on monitored data could determine the prior weights, and then they were combined to obtain a comprehensive weight, SEM can find out the relationship between indicators, which other methods could not. At the same time, based on the advantages both distance and shape similarity could obtain hybrid similarity, then determine water quality level by maximum hybrid similarity value between standard cloud and comprehensive cloud. This approach is utilized to a part of Minjiang River in China, and compared to other three methods, which are Single Factor (SF) method, Comprehensive Pollution Index (CPI) method, and Grey Relation Analysis (GRA) model. The results show that CHS and GRA are in accordance with each other, and they are more reasonable than other two methods because of considering the uncertainty of water quality. This approach was effective to evaluate the water quality level, as a reference for water quality management and applications.

Keyphrases: Entropy, Structure Equation Model, cloud model, hybrid similarity, water quality evaluation

@booklet{EasyChair:1296,
  author    = {Yin Huimin and Zhao Chunlan and Wang Bing and Wang Xiaobo and Li Yi and He Ting},
  title     = {A cloud model based on hybrid similarity approach for water quality evaluation},
  howpublished = {EasyChair Preprint 1296},
  year      = {EasyChair, 2019}}