An Approach for Selecting a Model for the Assessment of Potentially Contaminated Sites

An Approach for Selecting a Model for the Assessment of Potentially Contaminated Sites
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潜在污染场地评估模型的选择方法

DOI:
10.1111/gwat.13204
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发表时间:
2022
期刊:
影响因子:
2.6
通讯作者:
Chahar
Chahar
中科院分区:
地球科学3区
文献类型:
--
作者:
Daulat;Nogueira Hernandes;Chahar

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潜在污染场地(PCS)的评估可能是昂贵的,因此,需要简单和要求较低的方法和模型。这项工作试图提供一种方法,可以帮助选择最合适的PCS模型。所开发的方法使用100多个现场数据,以评估四个测试模型(分析/经验),提供最大羽流长度(Lmax),这是用来作为一个主要的模型在这项工作中的排名量。现场数据的分析表明,现场羽流长度(Lf)遵循对数正态分布。随后,Lmaxis将阈值概率描述为低估、高估和过度高估。赤池信息准则(AIC)和层次分析法(AHP)被认为是支持阈值方法的结果。将经典的AIC修改为AICmod,以拟合由Lf和Lmax之间的差表示的项。此外,将作为主观权重的乘积的阈值因子添加到AICmod。使用LfandLmax,AICmod提供了测试模型的不同排名。对于AHP方法,拟合优度、低估、过度高估和模型复杂性是四个选择标准。与AICmod类似,AHP方法提供了测试模型的不同排名。最佳拟合模型的最终决定,可以在用户的标准,在这项工作中开发的计划。
Assessment of potentially contaminated sites (PCS) can be expensive; hence, simple and less demanding methods and models are required. This work attempts to provide an approach that can aid in selecting the most appropriate model for the PCS. The developed method uses over 100 field site data to evaluate four test models (analytical/empirical) that provide the maximum plume length (Lmax), which is used as a principal model ranking quantity in this work. Analysis of site data shows that field plume length (Lf) follows a log‐normal distribution. Subsequently,Lmaxis delineated with respect toLfusing a threshold probability as underestimating, overestimating, and overly‐overestimating. Akaike information criterion (AIC) and analytical hierarchy process (AHP) are considered to support the threshold approach results. The classical AIC is modified (to AICmod) to fit the term represented by the difference betweenLfandLmax.Additionally, the threshold factors as a product of subjective weights are added to the AICmod. UsingLfandLmax, the AICmodprovides a distinct ranking of the test models. For the AHP approach, the goodness of fit, underestimation, overly overestimation, and model complexity are the four chosen criteria. Similar to AICmod, the AHP approach provides a distinct ranking of the test models. The final decision on the best fitting model can be made on user criteria following the scheme developed in this work.
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