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

Author

Listed:
  • Yuanchang Zhong
  • Liang Zhang
  • Shaojing Xing
  • Fachuan Li
  • Beili Wan

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.

Suggested Citation

  • Yuanchang Zhong & Liang Zhang & Shaojing Xing & Fachuan Li & Beili Wan, 2014. "The Big Data Processing Algorithm for Water Environment Monitoring of the Three Gorges Reservoir Area," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:698632
    DOI: 10.1155/2014/698632
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    References listed on IDEAS

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    1. Shen Yin & Xin Gao & Hamid Reza Karimi & Xiangping Zhu, 2014. "Study on Support Vector Machine‐Based Fault Detection in Tennessee Eastman Process," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    2. Shen Yin & Guang Wang & Xu Yang, 2014. "Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing data," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(7), pages 1375-1382, July.
    3. Shen Yin & Xin Gao & Hamid Reza Karimi & Xiangping Zhu, 2014. "Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-8, April.
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