Spatial interpolation of severely skewed data with several peak values by the approach integrating kriging and triangular irregular network interpolation |
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Authors: | Chunfa Wu Jiaping Wu Yongming Luo Haibo Zhang Ying Teng Stephen D DeGloria |
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Institution: | (1) Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China;(2) College of Environmental and Resources Sciences, Zhejiang University, Hangzhou, 310029, China;(3) Department of Crop and Soil Sciences, Cornell University, Ithaca, NY 14853, USA; |
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Abstract: | It was not unusual in soil and environmental studies that the distribution of data is severely skewed with several high peak
values, which causes the difficulty for Kriging with data transformation to make a satisfied prediction. This paper tested
an approach that integrates kriging and triangular irregular network interpolation to make predictions. A data set consisting
of total Copper (Cu) concentrations of 147 soil samples, with a skewness of 4.64 and several high peak values, from a copper
smelting contaminated site in Zhejiang Province, China. The original data were partitioned into two parts. One represented
the holistic spatial variability, followed by lognormal distribution, and then was interpolated by lognormal ordinary kriging.
The other assumed to show the local variability of the area that near to high peak values, and triangular irregular network
interpolation was applied. These two predictions were integrated into one map. This map was assessed by comparing with rank-order
ordinary kriging and normal score ordinary kriging using another data set consisting of 54 soil samples of Cu in the same
region. According to the mean error and root mean square error, the approach integrating lognormal ordinary kriging and triangular
irregular network interpolation could make improved predictions over rank-order ordinary kriging and normal score ordinary
kriging for the severely skewed data with several high peak values. |
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