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首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >Water Quality Assessment in the Harbin Reach of the Songhuajiang River (China) Based on a Fuzzy Rough Set and an Attribute Recognition Theoretical Model
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Water Quality Assessment in the Harbin Reach of the Songhuajiang River (China) Based on a Fuzzy Rough Set and an Attribute Recognition Theoretical Model

机译：基于模糊粗糙集和属性识别理论模型的松花江哈尔滨水质评价

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摘要

A large number of parameters are acquired during practical water quality monitoring. If all the parameters are used in water quality assessment, the computational complexity will definitely increase. In order to reduce the input space dimensions, a fuzzy rough set was introduced to perform attribute reduction. Then, an attribute recognition theoretical model and entropy method were combined to assess water quality in the Harbin reach of the Songhuajiang River in China. A dataset consisting of ten parameters was collected from January to October in 2012. Fuzzy rough set was applied to reduce the ten parameters to four parameters: BOD5, NH3-N, TP, and F. coli (Reduct A). Considering that DO is a usual parameter in water quality assessment, another reduct, including DO, BOD5, NH3-N, TP, TN, F, and F. coli (Reduct B), was obtained. The assessment results of Reduct B show a good consistency with those of Reduct A, and this means that DO is not always necessary to assess water quality. The results with attribute reduction are not exactly the same as those without attribute reduction, which can be attributed to the α value decided by subjective experience. The assessment results gained by the fuzzy rough set obviously reduce computational complexity, and are acceptable and reliable. The model proposed in this paper enhances the water quality assessment system.
机译：在实际水质监测过程中需要获取大量参数。如果在水质评估中使用所有参数，则计算复杂度肯定会增加。为了减少输入空间的维数，引入了模糊粗糙集进行属性约简。然后，结合属性识别理论模型和熵值法对松花江哈尔滨河段水质进行评价。 2012年1月至10月收集了由10个参数组成的数据集。应用模糊粗糙集将10个参数减少为4个参数：BOD5，NH3-N，TP和F. coli（还原A）。考虑到DO是水质评估中的常用参数，因此获得了另一种还原方法，包括DO，BOD5，NH3-N，TP，TN，F和大肠杆菌（还原方法B）。还原B的评估结果与还原A的评估结果具有很好的一致性，这意味着DO不一定总是需要评估水质。具有属性减少的结果与没有属性减少的结果并不完全相同，这可以归因于主观经验决定的α值。通过模糊粗糙集获得的评估结果明显降低了计算复杂度，并且是可接受和可靠的。本文提出的模型完善了水质评估系统。

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