- 學位論文
條件拉丁超立方抽樣應用於土壤重金屬資料抽樣與空間分佈模擬
Conditioned Latin hypercube sampling in heavy metal sampling and spatial distribution simulation
摘要
條件拉丁超立方抽樣法是一種以啟發式演算法，其在現有的資料空間中找出符合拉丁超立方抽樣特徵空間的抽樣方式。拉丁超立方抽樣法是一種分層隨機採樣的方法，能有效的選出符合原始資料分布的樣本，常應用於敏感度及不確定性分析。本研究應用條件拉丁超立方抽樣法於彰化地區土壤重金屬採樣資料抽樣，希望能找出的樣點中鉻、銅、鎳、鋅四種重金屬能符合原始採樣資料的統計特性及空間特性的樣本，以減少後續監測、復育所需要的樣本數，進而減少實驗室分析的成本，然而條件拉丁抽樣法並未考慮採樣點資料的空間分佈，因此本研究發展出分區條件拉丁超立方抽樣，首先於抽樣過程中將研究區分區，以期所選取的樣本於空間特性上能更接近於原始資料之空間特性及分佈，並與隨機抽樣方式及條件拉丁抽樣法進行空間特性比較。並且將原始資料與三種不同抽樣方式所得的資料以逐步指標模擬法模擬研究區內重金屬濃度空間分布情形，以及計算局部和空間不確定性。結果顯示以條件拉丁抽樣法所選出的樣點其重金屬的統計特性及空間特性皆較隨機抽樣法更接近原始資料的統計特性及空間特性，而逐步指標模擬的結果顯示條件拉丁抽樣法所選出之樣點可以保留汙染高風險區，而分區條件拉丁抽樣法不只能保留汙染高風險區的分布，更能降低高不確性區的分布。
並列摘要
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.