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Application of improved extension evaluation method to water quality evaluation

机译:改进的扩展评价法在水质评价中的应用

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摘要

The extension evaluation method (EEM) has been developed and applied to evaluate water quality. There are, however, negative values in the correlative degree (water quality grades from EEM) after the calculation. This is not natural as the correlative degree is essentially an index based on grades (rankings) of water quality by different methods, which are positive. To overcome this negativity issue, the interval clustering approach (ICA) was introduced, which is based on the grey clustering approach (GCA) and interval-valued fuzzy sets. However, the computing process and formulas of ICA are rather complex. This paper provides a novel method, i.e., improved extension evaluation method, so as to avoid negative values in the correlative degree. To demonstrate our proposed approach, the improved EEM is applied to evaluate the water quality of three different cross-sections of the Fen River, the second major branch river of the Yellow River in China and the Han Jiang River, one of the major branch rivers of the Yangtse River in China. The results of the improved evaluation method are basically the same as the official water quality. The proposed method possesses also the same merit as the EEM and ICA method, which can be applied to assess water quality when the levels of attributes are defined in terms of intervals in the water quality criteria. Existing methods are mostly applicable to data in the form of single numeric values.
机译:已经开发了扩展评估方法(EEM),并将其用于评估水质。但是,计算后相关度(EEM的水质等级)为负值。这是不自然的,因为相关度本质上是通过不同方法得出的,基于水质等级(等级)的指数,这是肯定的。为了克服此消极问题,引入了基于灰色聚类方法(GCA)和区间值模糊集的区间聚类方法(ICA)。但是,ICA的计算过程和公式相当复杂。本文提供了一种新颖的方法,即改进的扩展评价方法,以避免相关度为负值。为了证明我们提出的方法,将改进的EEM应用于评价t​​he河,中国黄河的第二大支流和汉江(其中的主要支流之一)的三个不同断面的水质。中国长江改进的评估方法的结果与官方水质基本相同。所提出的方法还具有与EEM和ICA方法相同的优点,当根据水质标准中的间隔来定义属性级别时,可以将其应用于评估水质。现有方法大多数适用于单个数值形式的数据。

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