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Water Quality Prediction Based on a Novel Fuzzy Time Series Model and Automatic Clustering Techniques

  • Hui Meng
  • Guoyin WangEmail author
  • Xuerui Zhang
  • Weihui Deng
  • Huyong Yan
  • Ruoran Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9436)

Abstract

In recent years, Fuzzy time series models have been widely used to handle forecasting problems, such as forecasting exchange rates, stock index and the university enrollments. In this paper, we present a novel method for fuzzy forecasting designed with the use of the two key techniques, namely automatic clustering and the probabilities of trends of fuzzy trend logical relationships. The proposed method mainly utilizes the automatic clustering algorithm to partition the universe of discourse into different lengths of intervals, and calculates the probabilities of trends of fuzzy trend logical relationships. Finally, it performs the forecasting based on the probabilities that were obtained in the previous stages. We apply the presented method for forecasting the water temperature and potential of hydrogen of the Shing Mun River, Hong Kong. The experimental results show that the proposed method outperforms Chen’s and the conventional methods.

Keywords

Water quality prediction Fuzzy sets Fuzzy time series Fuzzy logical relationship Automatic clustering algorithm 

Notes

Acknowledgments

This work is supported by the National Science and Technology Major Project (2014ZX07104-006) and the Hundred Talents Program of CAS (NO. Y21Z110A10)

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Hui Meng
    • 1
  • Guoyin Wang
    • 1
    Email author
  • Xuerui Zhang
    • 1
  • Weihui Deng
    • 1
  • Huyong Yan
    • 1
  • Ruoran Jia
    • 1
  1. 1.Big Data Mining and Applications Center, Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqingChina

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