Du Xiangjun, Shao Fengjing, Wu Shunyao, Zhang Hanlin, Xu Si
College of Automation Engineering, Qingdao University, Qingdao, 266071, China.
College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China.
Environ Monit Assess. 2017 Jul;189(7):335. doi: 10.1007/s10661-017-6035-y. Epub 2017 Jun 13.
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 grouping 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.
水质评估对于海洋富营养化评估、有害藻华预测及环境保护至关重要。以往研究已开发出许多用于水质评估的数值建模方法和数据驱动方法。聚类分析作为一种广泛用于数据分组的方法，也已被采用。然而，水质变量之间存在复杂的相关性，这些相关性在水质评估中起着重要作用，但一直被忽视。本文分析了水质变量之间的相关性，并提出了一种基于马氏距离的层次聚类分析的水质评估替代方法。此外，我们对采集自中国渤海和北黄海近岸海域的水质数据进行聚类，并将聚类结果应用于水质评估。为评估有效性，我们还采用以往研究广泛采用的基于欧氏距离的聚类分析对水质数据进行聚类。结果表明，我们的方法更适用于具有许多相关水质变量的水质评估。据我们所知，这是首次尝试将马氏距离应用于近岸水质评估。
