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基于模糊逻辑的地下水脆弱性评估:以伊朗 Zayandehrood 含水层为例。

Groundwater vulnerability assessment using fuzzy logic: a case study in the Zayandehrood aquifers, Iran.

机构信息

Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran.

出版信息

Environ Manage. 2013 Jan;51(1):267-77. doi: 10.1007/s00267-012-9960-0. Epub 2012 Nov 2.

Abstract

Groundwater is an important source of water, especially in arid and semi-arid regions where surface water is scarce. Groundwater pollution in these regions is consequently a major concern, especially as pollution control and removal in these resources are not only expensive but at times impossible. It is, therefore, essential to prevent their contamination in the first place by properly identifying vulnerable zones. One method most commonly used for evaluating groundwater pollution is the DRASTIC method, in which the Boolean logic is used to rank and classify the parameters involved. Problems arise, however, in the application of the Boolean logic. In this paper, the fuzzy logic has been used to avoid the problems. For this purpose, three critical cases of minimum, maximum, and mean values have been considered for the net recharge parameter. The process has been performed on the Zayandehrood river basin aquifers. The fuzzy-DRASTIC vulnerability map thus obtained indicates that the western areas of the basin generally have the maximum pollution potential followed by the areas located in the east. The central parts of the study area are found to have a low pollution potential. Finally, two sensitivity analyses are performed to show the significance of each value of the net recharge parameter in the calculation of vulnerability index.

摘要

地下水是一种重要的水源,特别是在干旱和半干旱地区,地表水稀缺。因此,这些地区的地下水污染是一个主要关注点,特别是因为在这些资源中进行污染控制和去除不仅昂贵,而且有时是不可能的。因此,通过正确识别脆弱区域,首先防止其受到污染至关重要。评估地下水污染最常用的方法之一是 DRASTIC 方法,其中布尔逻辑用于对所涉及的参数进行排序和分类。然而,在应用布尔逻辑时会出现问题。在本文中,使用模糊逻辑来避免这些问题。为此,对净补给参数考虑了最小值、最大值和平均值的三个关键情况。该过程已在 Zayandehrood 河流域含水层上执行。由此获得的模糊-DRASTIC 脆弱性图表明,流域的西部地区通常具有最大的污染潜力,其次是东部地区。研究区域的中心部分被发现具有低污染潜力。最后,进行了两次敏感性分析,以显示净补给参数的每个值在脆弱性指数计算中的重要性。

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