25 July 2007 Application and comparing of IDW and Kriging interpolation in spatial rainfall information
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Geostatistics analyst is based on the fundamental geographic principal, namely, things that are closer together tend to be more alike than things that are farther apart and widely used in many fields. In this paper, taking Ganjiang Drainage as sample region, we select IDW, Ordinary Kriging and Simple Kriging interpolation of geostatistics analyst to interpolate rainfall data and use cross-validation to compare the results of interpolation. In order to find out the most suitable interpolation method, we respectively use different interpolation methods with same parameters, same method with different semivariogram model as well as considering trend influence and anisotropy to interpolate the rainfall data. Comparing the results, we draw the following conclusions: (1) Under the premise of knowing mean, Simple Kriging owns the highest interpolation precision. (2) Kriging has fine feature to reflect rainfall trend changing of larger-scale extent. On the contrary, IDW can depict local detailed changing well. (3) Rainfall data exists weak autocorrelation. (4) Exponential model of semivariogram has the highest precision than others. (5) Ignoring trend influence and anisotropy will not decrease the precision of interpolation.
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Yunfei Shi, Yunfei Shi, Lin Li, Lin Li, Lingling Zhang, Lingling Zhang, } "Application and comparing of IDW and Kriging interpolation in spatial rainfall information", Proc. SPIE 6753, Geoinformatics 2007: Geospatial Information Science, 67531I (25 July 2007); doi: 10.1117/12.761859; https://doi.org/10.1117/12.761859

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