Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms

Ming Sheng Yeh, Yu Pin Lin*, Liang-Jeng Chang

*此作品的通信作者

研究成果: Article同行評審

50 引文 斯高帕斯(Scopus)

摘要

The optimal selection of monitoring wells is a major task in designing an information-effective groundwater quality monitoring network which can provide sufficient and not redundant information of monitoring variables for delineating spatial distribution or variations of monitoring variables. This study develops a design approach for an optimal multivariate geostatistical groundwater quality network by proposing a network system to identify groundwater quality spatial variations by using factorial kriging with genetic algorithm. The proposed approach is applied in designing a groundwater quality monitoring network for nine variables (EC, TDS, Cl-, Na, Ca, Mg, SO 4 2- , Mn and Fe) in the Pingtung Plain in Taiwan. The spatial structure results show that the variograms and cross-variograms of the nine variables can be modeled in two spatial structures: a Gaussian model with ranges 28.5 km and a spherical model with 40 km for short and long spatial scale variations, respectively. Moreover, the nine variables can be grouped into two major components for both short and long scales. The proposed optimal monitoring design model successfully obtains different optimal network systems for delineating spatial variations of the nine groundwater quality variables by using 20, 25 and 30 monitoring wells in both short scale (28.5 km) and long scale (40 km). Finally, the study confirms that the proposed model can design an optimal groundwater monitoring network that not only considers multiple groundwater quality variables but also monitors variations of monitoring variables at various spatial scales in the study area.

原文English
頁(從 - 到)101-121
頁數21
期刊Environmental Geology
50
發行號1
DOIs
出版狀態Published - 1 5月 2006

指紋

深入研究「Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms」主題。共同形成了獨特的指紋。

引用此