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Developing a fuzzy optimization model for groundwater risk assessment based on improved DRASTIC method

机译:基于改进的DRASTIC方法开发地下水风险评估的模糊优化模型

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Groundwater pollution is a serious threat to water resources which attracts hydrologists attention for sustainable management. Comprehensive assessment of groundwater vulnerability, however, requires uncertainty analysis in conjunction with a proper method to determine prone areas to contaminants. This paper provides a new fuzzy optimization methodology using improved DRASTIC method to model groundwater vulnerability risk assessment which considers uncertainties embedded in the input parameters, optimizes the weights and modifies the rates of the model simultaneously. To better represent hydrogeological characteristics of an area, the rating scores of original DRASTIC method is modified using Wilcoxon test considering nitrate concentration (as the main interfering pollutant in the study area). Spearman correlation coefficient between vulnerability indices and nitrate concentration is used as a factor to measure how wellimproved DRASTIC performs for vulnerability assessment as compared to the original method. The results show that the correlation coefficient significantly increased from 0.573 to 0.789. To address uncertainties associated with the input and output of the model, reduced fuzzy transformation method (FTM) is empowered by genetic algorithm (GA) to consider uncertainties associated with input parameters as well as optimizing weights of improved DRASTIC model. The results show how correlation coefficient changes at different uncertainty levels. Considering uncertainties in the inputs, correlation coefficient changes from 0.746 to almost 0.758at -cut level equal to 0 in comparison with that of equal to 1. Comparison of the risk maps of improved DRASTIC and fuzzy model at different uncertainty levels reveals that the model performs robustly under uncertainties mainly since the vulnerability trend and more importantly the severity of vulnerability indices have not changed remarkably. Based on these maps, east and southeastern parts of the study area are highly susceptible to contamination where intense industrial and agricultural activities can be seen. This framework provides helpful information for decision-makers to consider risk assessment at different uncertainty levels as it offers a continuous range of vulnerability indices rather than fixed ones. Also, vulnerability risk assessment maps demonstrate vulnerability trend throughout the area for further controlling or remedying actions of groundwater network.
机译:地下水污染是对水资源的严重威胁,吸引了水文学家对可持续管理的关注。但是,对地下水脆弱性的综合评估需要进行不确定性分析,并结合适当的方法来确定易受污染的区域。本文提供一种使用改进的DRASTIC方法对地下水脆弱性风险评估进行建模的新的模糊优化方法,该方法考虑了输入参数中嵌入的不确定性,同时优化了权重并同时修改了模型的费率。为了更好地表示一个地区的水文地质特征,考虑到硝酸盐浓度(作为研究区域的主要干扰污染物),使用Wilcoxon试验修改了原始DRASTIC方法的评分。与原始方法相比,脆弱性指数和硝酸盐浓度之间的Spearman相关系数被用作衡量DRASTIC在脆弱性评估方面的表现如何。结果表明,相关系数从0.573显着增加到0.789。为了解决与模型的输入和输出相关的不确定性,遗传算法(GA)赋予了简化的模糊变换方法(FTM)来考虑与输入参数相关的不确定性以及优化改进的DRASTIC模型的权重。结果表明,相关系数如何在不同的不确定性水平上变化。考虑到输入中的不确定性,相关系数在等于0时与在等于1时相比从0.746变为几乎0.758。在不同不确定性水平上改进的DRASTIC和模糊模型的风险图的比较表明,该模型执行在不确定性条件下保持强大的状态主要是因为脆弱性趋势,更重要的是脆弱性指数的严重性没有明显变化。根据这些地图,研究区域的东部和东南部极易受到污染,在那里可以看到强烈的工业和农业活动。该框架为决策者提供了一系列有用的信息,使他们可以考虑在不同的不确定性级别上进行风险评估,因为它提供了连续范围的脆弱性指数,而不是固定的脆弱性指数。此外,脆弱性风险评估图显示了整个地区的脆弱性趋势,可用于进一步控制或补救地下水网络的行为。

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