Abstract and Applied Analysis

The Big Data Processing Algorithm for Water Environment Monitoring of the Three Gorges Reservoir Area

Yuanchang Zhong, Liang Zhang, Shaojing Xing, Fachuan Li, and Beili Wan

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Abstract

Owing to the increase and the complexity of data caused by the uncertain environment, the water environment monitoring system in Three Gorges Reservoir Area faces much pressure in data handling. In order to identify the water quality quickly and effectively, this paper presents a new big data processing algorithm for water quality analysis. The algorithm has adopted a fast fuzzy C-means clustering algorithm to analyze water environment monitoring data. The fast clustering algorithm is based on fuzzy C-means clustering algorithm and hard C-means clustering algorithm. And the result of hard clustering is utilized to guide the initial value of fuzzy clustering. The new clustering algorithm can speed up the rate of convergence. With the analysis of fast clustering, we can identify the quality of water samples. Both the theoretical and simulated results show that the algorithm can quickly and efficiently analyze the water quality in the Three Gorges Reservoir Area, which significantly improves the efficiency of big data processing. What is more, our proposed processing algorithm provides a reliable scientific basis for water pollution control in the Three Gorges Reservoir Area.

Article information

Source
Abstr. Appl. Anal., Volume 2014 (2014), Article ID 698632, 7 pages.

Dates
First available in Project Euclid: 6 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1412606617

Digital Object Identifier
doi:10.1155/2014/698632

Zentralblatt MATH identifier
07022907

Citation

Zhong, Yuanchang; Zhang, Liang; Xing, Shaojing; Li, Fachuan; Wan, Beili. The Big Data Processing Algorithm for Water Environment Monitoring of the Three Gorges Reservoir Area. Abstr. Appl. Anal. 2014 (2014), Article ID 698632, 7 pages. doi:10.1155/2014/698632. https://projecteuclid.org/euclid.aaa/1412606617


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