Optimal design of groundwater quality monitoring network using Factorial kriging

• Main Authors: Wen Hung Hsu, 許文鴻
• Other Authors: Liang Cheng Chang
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/02862609354865627532

碩士 === 國立交通大學 === 土木工程系所 === 93 === In resent studies, geostatistical methods, as Kriging and Co-Kriging, have been applied to design the groundwater monitoring networks. These methods can determine an optimal network based on the spatial variability of a selected variable. However, since a representative variable is difficult to define, the conventional geostatistical methods will be difficult to apply directly when the monitoring network is used to monitor multi-variables. Besides, conventional geostatistical methods consider only single geostatistical structure represented by a variogram, and this may not accurately represent the geostatistical structures for an area with multiple regionalization structures. A Factorial Kriging is an integrated methodology consists of Multivariate Variogram Modeling, Principal Component Analysis and Co-Kringing method. Therefore, it can consider multi-scales geostatistical structures and solve a multi-variables problem by using the factor variables as a representative variable. This research develops an optimal design method to solve groundwater network design problems by combining the Factorial Kringing with Genatic Algorithm (GA). The proposed model is applied to design the groundwater monitoring network in Pingtung plain, Taiwan. The design considers nine groundwater quality variables and two scales of geostatistical structure represented by two variograms. One of the variogram is Gaussian type with an effective range of 28.5 km and the other is Spherical type with an effective range of 40 km. The model successfully obtains different optimal network designs with respect to 10, 20 and 30 wells. The study demonstrates that the proposed model can optimally design a complicated groundwater monitoring network that considers multiple groundwater quality variables and multiple scales of geostatistical structures.