关键词: 模式识别, 水质污染, 自组织映射神经网络, 随机森林

Abstract:

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-offitness 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.

Key words: pattern recognition, water pollution, self-organizing feature map, random forest