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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63932完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林裕彬(Yu-Pin Lin) | |
| dc.contributor.author | Yen-Yu Chen | en |
| dc.contributor.author | 陳彥佑 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:23:31Z | - |
| dc.date.available | 2014-08-19 | |
| dc.date.copyright | 2012-08-19 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-16 | |
| dc.identifier.citation | 參考文獻
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Brungard C.W., Boettinger J.L. (2010) Conditioned Latin Hypercube Sampling: Optimal Sample Size for Digital Soil Mapping of Arid Rangelands in Utah, USA. Digital Soil Mapping, Progress in Soil Science 2, 67-75. 23. Brus, D.J., de Gruijter, J.J., Van Groenigen, J.W. (2004) Designing purposive and random spatial coverage samples by the k-means clustering algorithm. Global Workshop on Digital Soil Mapping, Montpellier, France, September 2004. 24. Deutsch CV, Journel AG (1992) GSLIB. Geostatistical software library and user’s guide. Oxford University Press, New York, 340. 25. U.S. EPA, 2002, Guidance on Choosing a Sampling Design for Environmental Data Collection, EPA-240-R-02-005, Washington. 26. Goovaerts, P. (1997) Geostatistics for natural resources evaluation, Oxford University Press: New York, pp. 259-368. 27. Goovaerts, P. (2001) Geostatistical modelling of uncertainty in soil science. Geoderma, 103, 3-26. 28. Juang, K.W., and Lee, D. Y. (1998a) Simple Indicator Kriging for Estimating the Probability of Incorrectly Delineating Hazardous Areas in a Contaminated Site. Environmental Science & Technology, 32(17), 2487-2493. 29. Juang, K.W. and Lee, D.Y. (1998b) A Comparison of Three Kriging Methods Using Auxiliary Variables in Heavy-Metal Contaminated Soils. Journal of Environmental Quality, 27 (2), 355-363. 30. Journel, A.G. (1988) Nonparametric Geostatistics for risk and Additional Sampling Assessment. In L. Keith (ed.) Principles of environmental sampling, American Chemical Society, 45-72. 31. Juang, K.W., Chen, Y. S., Lee, D. Y. (2004) Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils. Environmental Pollution, 127, 229-238. 32. Lin, Y.P., Chang, T.K., Teng, T.P. (2001) Characterization of soil lead by comparing sequential Gaussian simulation, simulated annealing simulation and kriging methods. Environ Geol, 41(1), 189–199. 33. Lin, Y.P., Teng T.P., Chang T.K. (2002) Multivariate analysis of soil heavy metal pollution and landscape pattern in Changhua County in Taiwan. Landscape and Urban Planning, 6219-35. 34. Lin, Y.P. (2008) Simulating spatial distributions, variability and uncertainty of soil arsenic by geostatistical simulations in geographic information systems. The Open Environmental Journal, 2, 26-33. 35. Lin, Y.P., Cheng, B.Y., Shyu, G.S., Chang, T.K. (2010) Combining a finite mixture distribution model with indicator kriging to delineate and map the spatial patterns of soil heavy metal pollution in Chunghua County, central Taiwan. 36. Lin, Y.P., Chu, H.J., Huang, Y.L., Cheng, B.Y., Chang, T.K. (2010) Modeling spatial uncertainty of heavy metal content in soil by conditional Latin hypercube sampling and geostatistical simulation. Environmental Earth Sciences, ISSN: 1866-6280. 37. Matthew G. Falk, Robert J. Denham, Kerrie L. Mengersen (2011) Spatially stratified sampling using auxiliary information for geostatistical mapping. Environ Ecol Stat, 18:93–108, DOI 10.1007/s10651-009-0122-3 38. Minasny B., McBratney A.B. (2006) A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences, 32, 1378–1388. 39. Minasny, B., McBratney,A,B. Walvoort, D.J.J. (2007) The variance quadtree algorithm:Use for spatial sampling design. Computers & Geosciences, 33, 383–392. 40. Minasny B., McBratney A.B. (2010) Conditioned Latin Hypercube Sampling for Calibrating Soil Sensor Data to Soil Properties. PROXIMAL SOIL SENSING Progress in Soil Science, Volume 1, Part 2, 111-119. 41. van Groenigen, J.W., Siderius, W., Stein, A.(1999) Constrained optimisation of soil sampling for minimisation of the kriging variance. Geoderma,87 , 239–259. 42. Xu, C.G., He, H.S., Hu, Y.M., Chang, Y., Li, X.Z., Bu, R.C. (2005) Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation. Ecol Model , 185(2–4), 255–269 43. Zhao, Y.C., Shi, X.Z., Yu, D.S. et al. (2005) Uncertainty assessment of spatial patterns of soil organic carbon density using sequential indicator simulation, a case study of Hebei Province, China. Chemosphere, 59(11), 1527–1535. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63932 | - |
| dc.description.abstract | 土壤重金屬污染為現今重要的環境污染議題之一,由於重金屬污染對於自然環境及公眾健康有極大之危害,因此必須要持續監測並且對已污染區域進行整治,而為了在無背景調查資料時能夠了解重金屬污染之空間分布以界定污染之範圍,初次採樣時即需仰賴良好之採樣策略以確保具代表性樣本之取得,進而提升後續污染分布推估之準確性及整治之效率。條件拉丁超立方採樣法(cLHS)為結合退火程序及啟發式規則之採樣方法,可有效挑選出較符合原始資料特性之子樣本,但其並無考慮資料之空間分布。本研究以彰化縣內946筆土壤樣本之鉻、銅、鎳、鋅等四種重金屬濃度與環境變數為研究材料,應用條件拉丁超立方採樣法並結合中樣區網格分區以及灌區分區兩種空間分區策略,先利用羅吉斯回歸挑選出相關環境變數作為採樣法之輔助變數進行不同樣本數之採樣,同時引入地理統計理論進行空間模擬,最後計算多次模擬下採樣樣本與原始資料之平均值及空間變異度誤差,期望能藉此了解不同採樣策略以及採樣數下各子樣本之基本統計量與空間變異性重現情況。本研究架構可分為方法驗證及模擬全域採樣兩部分,驗證部分先由原始946點土壤重金屬資料以相關環境變數為基礎進行再採樣,由結果顯示條件拉丁超立方採樣確實能由相關環境變數中選取出代表性樣本;模擬全域採樣部分則是以原始946點土壤樣本進行1000次逐步指標模擬後,將採樣點位置對應至各模擬圖層並計算各圖層基本統計量與空間變異之平均絕對誤差值(MAE),最後由1000次誤差之平均值結果討論不同採樣策略及不同樣本數下時以相關環境變數進行初次監測採樣之成效。最後結果顯示平均值誤差受採樣數大小影響不明顯,而空間變異誤差則會隨採樣數增加而明顯下降;此外,當以條件拉丁超立方採樣法結合灌區分區策略時,整體上較能夠保留原始資料之統計特性與空間變異情況,可實際應用於重金屬污染區域之初次採樣監測。 | zh_TW |
| dc.description.abstract | Soil pollution of heavy metals is one of the most important environment issues, and it is necessary to monitor and proceed remediation for contaminated area duo to the serious impact of heavy metal pollution to the environment and public health concerns. An efficient sampling strategy is needed to know the correct spatial distribution of soil pollutants to delineate the contaminated area, and reduce the follow-up sampling points to decrease the remediation costs. Conditioned Latin Hypercube Sampling (cLHS) is a sampling method using search algorithm based on heuristic rules combined with an annealing schedule, and it is demonstrated that cLHS could accurately reproduce the original distribution of the environmental covariates. In this study, the original 946 sampling data of Cr, Cu, Ni and Zn and correlated ancillary information in Chand-Hua County are used, and cLHS with two stratifications are applied to resample original data by selected ancillary variables. The errors of statistical and spatial of resampled data are calculated to discuss the influence of different sampling strategies and sample sizes on reproducibility. After proving that representative sampling could be selected by ancillary data, 1000 times sequential indicator simulation (SIS) are carried out in this study with original data, and the mean average error (MAE) is used to evaluate the efficiency of sampling strategies and sample sizes with ancillary information on the first time sampling. The results reveal that error of average value is not affected obviously by the change of sample size. However, the error of spatial variance decreases when the sample size increases. In conclusion, cLHS combined with irrigation stratification can efficiently preserve the statistic characteristics and spatial structure of the original data. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:23:31Z (GMT). No. of bitstreams: 1 ntu-101-R99622023-1.pdf: 9346024 bytes, checksum: 46f212457a19b980425d976111bed28d (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 目錄
摘要 I Abstract II 目錄 III 圖目錄 V 表目錄 VI 一、前言 1 二、文獻回顧 3 2.1 土壤重金屬調查情形 3 2.2 土壤採樣方法 5 2.3 地理統計 9 三、研究理論與方法 13 3.1 研究區域與架構 13 3.2 條件拉丁超立方採樣法 19 3.3 地理統計 23 3.4 分區畫定策略 32 3.5 羅吉斯迴歸 32 3.6 誤差分析 34 四、結果與討論 35 4.1變數選取 35 4.2方法驗證 37 4.3無土壤資料採樣之結果分析 54 五、結論與建議 59 5.1 結論 59 5.2 未來建議 60 參考文獻 61 附錄一 驗證採樣點結果分布圖 64 附錄二 模擬全域採樣點結果分布圖 66 附錄三 四種重金屬1000次逐步指標模擬平均濃度圖 68 附錄四 1000次模擬中空間變異誤差最大之變異圖 70 附錄五 1000次模擬中空間變異誤差最小之變異圖 86 | |
| dc.language.iso | zh-TW | |
| dc.subject | 土壤重金屬污染 | zh_TW |
| dc.subject | 條件拉丁超立方採樣法 | zh_TW |
| dc.subject | 採樣策略 | zh_TW |
| dc.subject | 地理統計理論 | zh_TW |
| dc.subject | geostatistics | en |
| dc.subject | soil heavy metal pollution | en |
| dc.subject | conditioned Latin Hypercube Sampling | en |
| dc.subject | sampling stategy | en |
| dc.title | 條件拉丁超立方採樣法結合分區策略應用於土壤重金屬污染之初步監測 | zh_TW |
| dc.title | Applying conditioned Latin hypercube sampling combined with stratifications in initial soil sampling of heavy metals | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 童慶斌(Ching-Pin Tung),張尊國(Tsun-kuo Chang),徐貴新(Guey-Shin Shyu) | |
| dc.subject.keyword | 土壤重金屬污染,條件拉丁超立方採樣法,採樣策略,地理統計理論, | zh_TW |
| dc.subject.keyword | soil heavy metal pollution,conditioned Latin Hypercube Sampling,sampling stategy,geostatistics, | en |
| dc.relation.page | 101 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-08-16 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
| 顯示於系所單位: | 生物環境系統工程學系 | |
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