Conditioned Latin hypercube sampling in heavy metal sampling and spatial distribution simulation
Date Issued
2010
Date
2010
Author(s)
Huang, Yu-Long
Abstract
Conditioned Latin hypercube sampling is a sampling method using heuristic algorithm to find out the data in the incumbent data space which conjoint the eigenspace of Latin hypercube sampling. Latin hypercube (LHS) is a stratified random sampling approach which can proceed the sampling technique with the original distribution. The research aims to resample the heavy metal in soil at Chang-Hua County by conditioned Latin hypercube sampling (LHS) technique, and with expectation to diminish the sampling number to lower the cost of laboratorial analysis for cupper (Cu), chromium (Cr), nickel (Ni), and zinc (Zn) with their original statistical distributions. In the meanwhile, there is no consideration in spatial aspect for sampling sites in conditioned Latin hypercube sampling (cLHS). So the incorporation of spatial data , which is regarded as the spatial cLHS, might be able to drive the data closer to their original spatial allocation. Afterwards, the sampling efficiency for LHS, cLHS, and spatial cLHS were fully examined. The spatial distribution and uncertainty of each technique, including original data without sampling, were evaluated by the sequential indicator simulation (SIS). The result showed that the spatial cLHS could better imitate the distribution and spatial allocation of the original data. And the result of SIS showed that the sampled data from cLHS could only preserve the risky area of pollution, but the ones from spatial cLHS could even lower the uncertainty.
Subjects
Heavy metal
Spatial uncertainty
Conditioned Latin hypercube sampling
Conditional simulation
soil Pollution
Type
thesis
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