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Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance

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Abstract

Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grou** data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [NO. 41476101], and we also thank North China Sea Branch of State Oceanic Administration for providing water quality monitoring data. In addition, we would like to thank the anonymous reviewers for their constructive comments on the manuscript.

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Authors and Affiliations

  1. College of Automation Engineering, Qingdao University, Qingdao, 266071, China

    **angjun Du

  2. College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China

    **angjun Du, Feng**g Shao, Shunyao Wu, Hanlin Zhang & Si Xu

  3. Institute of Complexity Science, Qingdao University, Qingdao, 266071, China

    Feng**g Shao

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  1. **angjun Du
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Correspondence to Feng**g Shao.

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Du, X., Shao, F., Wu, S. et al. Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Environ Monit Assess 189, 335 (2017). https://doi.org/10.1007/s10661-017-6035-y

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