结合PCA和模糊最优模型的地统计学方法在水质监测网采样点综合评价中的应用。

Journal of Environmental Monitoring Pub Date : 2012-12-01 Epub Date: 2012-10-29 DOI:10.1039/c2em30372h
Chunping Ou, André St-Hilaire, Taha B M J Ouarda, F Malcolm Conly, Nicole Armstrong, Bahaa Khalil, Sandra Proulx-McInnis
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引用次数: 26

摘要

评价取样地点的适当性是验证有效和高效率的水质监测网络的一个重要方面。提出了两种地质统计学方法(例如kriging和Moran's I)来评估多个采样位置。将地统计学方法与主成分分析(PCA)和模糊最优模型(FOM)相结合,建立了一个灵活、全面的多变量多采样点选择框架。FOM用于综合评价多主成分和多种地质统计方法。这些综合方法成功地应用于2006 - 2010年加拿大温尼伯湖两个独立的水质监测网络(WQMNs)的评价,分别包括14个和30个站点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Coupling geostatistical approaches with PCA and fuzzy optimal model (FOM) for the integrated assessment of sampling locations of water quality monitoring networks (WQMNs).

The assessment of the adequacy of sampling locations is an important aspect in the validation of an effective and efficient water quality monitoring network. Two geostatistical approaches (e.g., kriging and Moran's I) are presented to assess multiple sampling locations. A flexible and comprehensive framework was developed for the selection of multiple sampling locations of multiple variables which was accomplished by coupling geostatistical approaches with principal component analysis (PCA) and fuzzy optimal model (FOM). The FOM was used in the integrated assessment of both multiple principal components and multiple geostatistical approaches. These integrated methods were successfully applied to the assessment of two independent water quality monitoring networks (WQMNs) of Lake Winnipeg, Canada, which respectively included 14 and 30 stations from 2006 to 2010.

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来源期刊
Journal of Environmental Monitoring
Journal of Environmental Monitoring 环境科学-分析化学
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