利用自组织映射法对香港地表水水质的描述性特征
Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map.
作者信息
An Yan, Zou Zhihong, Li Ranran
机构信息
School of Economics and Management, Beihang University, Beijing 100191, China.
出版信息
Int J Environ Res Public Health. 2016 Jan 8;13(1):115. doi: 10.3390/ijerph13010115.
In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009-2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data.
在本研究中，主成分分析（PCA）和自组织映射（SOM）被用于分析从吐露港及赤门水质管制区（香港）的河流水质监测站获取的复杂数据集，涵盖2009年至2011年期间。PCA最初用于识别非线性和复杂地表水水质参数中的主成分（PC）。SOM在PCA之后进行，用于分析参数之间的复杂关系和行为。结果表明，PCA将多维参数简化为四个重要的主成分，这些主成分是原始参数的组合。通过成分平面中的模式分析明确显示了参数的正相关和负相关关系。研究发现，PCA和SOM是捕获和分析多变量、复杂且非线性相关的地表水水质数据行为的有效工具。
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