scholarly journals Interpolation Parameters in Inverse Distance-Weighted Interpolation Algorithm on DEM Interpolation Error

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhiqiang Liu ◽  
Bo Xu ◽  
Bo Cheng ◽  
Xiaomei Hu

Although DEM occupies an important basic position in spatial analysis, so far, the quality of DEM modeling has still not reached a satisfactory accuracy. This research mainly discusses the influence of interpolation parameters in the inverse distance-weighted interpolation algorithm on the DEM interpolation error. The interpolation parameters to be studied in this paper are the number of search points, the search direction, and the smoothness factor. In order to study the optimization of IDW parameters, the parameters that have uncertain effects on DEM interpolation are found through analysis, such as the number of search points and smoothing factor. This paper designs an experiment for the optimization of the interpolation parameters of the polyhedral function and finds the optimal interpolation parameters through experimental analysis. Of course, the “optimum” here is not the only one, but refers to different terrain areas, which makes the interpolation results relatively good. The selection of search points will be one of the research focuses of this article. After determining the interpolation algorithm, the kernel function is also one of the important factors that affect the accuracy of DEM. The value of the smoothing factor in the kernel function has always been the focus of DEM interpolation research. Different terrains, different interpolations, and functions will have different optimal smoothing factors. The search direction is to ensure that the sampling points are distributed in all directions when the sampling points are sparse and to improve the contribution rate of the sampling points to the interpolation points. The selection of search shape is to improve computing efficiency and has no effect on DEM accuracy; the search radius is mainly controlled by the number of search points, and there are two methods: adaptive search radius and variable length search radius. When the weight coefficient k = 1 , 2 , 3 , 4 , the number of sampling points involved in the interpolation calculation is different, and the error in the residual varies greatly, and both increase with the increase of the number of sampling points in the parameter interpolation calculation. This research will help improve the quality evaluation of DEM.

2003 ◽  
Vol 34 (5) ◽  
pp. 413-426 ◽  
Author(s):  
Antti Taskinen ◽  
Hannu Sirviö ◽  
Bertel Vehviläinen

The present approach for daily temperature interpolation of the Watershed Simulation and Forecasting System of the Finnish Environment Institute is based on the inverse distance weighted interpolation. In order to improve the calculation, three alternative methods were tested: 1) modified inverse distance weighted model, 2) regression with dummy variables for taking into account time and 3) regression equation calibrated for each day. The regression model calibrated for each day proved to be the most promising model. By average, the difference between the accuracy of it and the inverse distance weighted methods wasn't big but some indication was found that in single cases it can make a difference. The estimated parameters were found to have realistic physical meanings.


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