Clustering of Lake Variables Based on Pattern Recognition Method

被引：0

作者：

Ren T. ^{[1
]}

Liang Z. ^{[1
]}

Chen H. ^{[1
]}

Liu Y. ^{[1
]}

机构：

[1] College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences Ministry of Education, Peking University, Beijing

来源：

Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis Pekinensis | 2019年 / 55卷 / 02期

关键词：

Pattern recognition; Random forest; Self-organizing feature map; Water pollution;

D O I：

10.13209/j.0479-8023.2019.001

中图分类号：

学科分类号：

摘要：

The self-organizing feature map (SOFM) and random forest (RF) method were integrated to recognize water quality patterns of nine water quality indicators for 63 lakes in China for 11 years (5110 data). The SOFM was built firstly to cluster lakes to identify the pollution conditions. Then, the RF was used to explore the good-of-fitness of water quality variables on the clustering result and to determine the important water quality indicators. The result of SOFM shows that the lakes can be clustered into three types. And the result of RF shows that permanganate index and chlorophyll a can determine the pollution condition when the classification accuracy is 80%. The integrated method can identify the water quality indicators reflecting the pollution conditions from complex data. In practice, the method can be used to determine the pollution conditions and direct the monitoring indicators. © 2019 Peking University.

引用

页码：335 / 341

页数：6

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