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《Journal of Geo-Information Science》 2017-08
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Kriging Interpolation Method Optimized by Multi-scale Least Squares Support Vector Machine

CHE Lei;WANG Haiqi;FEI Tao;YAN Bin;LIU Yu;GUI Li;CHEN Ran;ZHAI Wenlong;School Of Geosciences, China University of Petroleum (East China);China Research Institute of Radiowave Propagation Qingdao Branch;  
Kriging interpolation method realizes spatial weighted estimation that meets the unbiasedness and optimality according to the position relationship between the estimated location sites and the known sample sites and regionalized variable spatial correlation. Traditional theoretical model shape is fixed and chosen with subjectivity, which can't reflect the changing trend and multi-scale spatial characteristics. The choice of scale and the treatment of scale effects also need to be considered. To solve the problems above, we propose a method of kriging interpolation optimized by multi-scale least squares support vector machine(LS-SVM), which provides a new idea for fitting experimental variogram. Starting from the changing trend of the actual sample data, least squares support vector machine fits experimental variogram and the results conform to the spatial changing trend of data itself. Secondly, the wavelet kernel as the LS-SVM kernel function, parameters can be adjusted according to different parts of the nuclear, which is flexible and variable. Finally, the multi-scale wavelet kernel using wavelet multi-resolution characteristics, can reflect the different details of spatial changes, to avoid the single scale LS-SVM ignoring the spatial details of the problem. Followed that, the experiment includes simulation and application. Experimental simulation mainly verifies scientific validity and accuracy by the optimized interpolation of multi-scale least squares support vector machine. Meanwhile, experimental application research of PM2.5concentrations of temporal and spatial distribution provides the theoretical basis for city ecological protection and controlling. Final results show that kriging interpolation algorithm optimized by multi-scale least squares support vector machine is superior to the traditional method and single scale optimized kriging interpolation algorithm. It would be better to depict the variation function and reflect the different scales of spatial changes in details to further improve the accuracy of the interpolation to some extent, which is an optional kriging interpolation method.
【Fund】: 国家自然科学基金项目(41471322);; 山东省自然科学基金项目(ZR2012DM010)
【CateGory Index】: X513
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