NDLI: Optimization of water quality monitoring section based on comprehensive hierarchical clustering

Content Provider	IEEE Xplore Digital Library
Author	Sen Peng Xiaofeng Lian Xiaoyi Wang Jiping Xu
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. & Inf. Eng., Beijing Technol. & Bus. Univ., Beijing, China (Sen Peng; Xiaofeng Lian; Xiaoyi Wang; Jiping Xu)
Abstract	In order to optimize the section layout of water quality monitoring, this paper proposes a new method based on comprehensive hierarchical clustering (CHC). Firstly, the method calculated the affinity-disaffinity relationship among the monitoring variables through 5 distance algorithms. Afterwards, the data set could be clustered automatically through 4 connection algorithms. Then taking the correlation coefficient as evaluation criteria, optimal hierarchical clustering algorithm was selected. Finally, with the corresponding optimal clustering tree matrix, the monitoring sections can be set optimally. In addition, the paper used student's t test to verify the result of optimization. The experimental results show that this method can reflect the water quality of whole area more efficiently, thus has good prospect.
Starting Page	124
Ending Page	129
File Size	255411
Page Count	6
File Format	PDF
e-ISBN	9781467376822
DOI	10.1109/FSKD.2015.7381927
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation coefficient Algorithm design and analysis student's t test section optimization evaluation criteria of correlation coefficient Clustering algorithms Lakes comprehensive hierarchical clustering (CHC) Water pollution water quality monitoring Monitoring Optimization
Content Type	Text
Resource Type	Article

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