Main Article Content
The topography of the earth's surface can be represented in GIS by DEM data. Surface modeling is the process of determining natural or artificial surfaces using one or more mathematical equations. A general surface modeling algorithm for all applications is not available, each method of creating a topographic surface has several advantages, disadvantages and depends on its processing direction. As such, experimenting, evaluating and selecting algorithms that are appropriate to the reality of the data and research area are necessary. Research paper, experimentally evaluating 4 Spline, IDW, Kriging and Natural Neighbor algorithms to model terrain on two map pieces representing different terrain types, the research results show that: the adapting each algorithm depends heavily on the terrain of each interpolation area. Spline interpolated terrain surfaces in more detail in ravine and valley areas; Natural Neighbor excels at matching the contours of data in all regions. IDW and Kriging algorithms have similar results and have lower accuracy than the above two methods, especially it is difficult to interpolate peaks and slopes. MAE, respectively, of high and medium hills and mountains are Spline (9.7, 10.3), NN (11.8, 10.1), IDW (13.0, 10.9), Kriging (13.3, 12.2).
Spatial interpolation, topographic modeling, DEM, DSM, accuracy
 P.A. Longley, et al., Geographic Information Systems and Science, John Wiley & Sons 3rd Edition (2010).
 Q. Weng, An evaluation of spatial interpolation accuracy of elevation data, in Progress in Spatial Data Handling, Springer-Verlag, Berlin (2006) 805-824.
 Pattathal Vijayakumar Arun, A comparative analysis of different DEM interpolation methods, The Egyptian Journal of Remote Sensing and Space Science 16.2 (2013) 133-139. https:// doi.org/10.1016/j.ejrs.2013.09.001.
 Paul Daniel Dumitru, Marin Plopeanu, Dragos Badea, Comparative study regarding the methods of interpolation, Recent advances in geodesy and Geomatics engineering 1 (2013) 45.
 Manuel Peralvo, David Maidment, Influence of DEM interpolation methods in drainage analysis, Gis Hydro 4 (2004) 4-7.
 A. Carrara, G. Bitelli, R. Carla, Comparison of techniques for generating digital terrain models from contour lines, International Journal of Geographical Information Science 11 (1997) 451-473.
 J.C. Guarneri, R.C. Weih Jr, Comparing Methods for Interpolation to Improve Raster Digital Elevation Models, Journal of the Arkansas Academy of Science 66 (2012) 77-81. https:// scholarworks.uark.edu/jaas/vol66/iss1/16.
 G.L. Heritage, D.J. Milan, Influence of survey strategy and interpolation model on DEM quality, Geomorphology. 112.3 (2009) 334-344. 10.1016/ j.geomorph. 2009.06.024.
 Dennis Weber, Evan Englund, Evaluation and comparison of spatial interpolators II, Mathematical Geology 26 (1994) 589-603.
 Besim Ajvazi, Kornél Czimber, A comparative analysis of different DEM interpolation methods in GIS: case study of Rahovec, Kosovo, Geodesy and Cartography 45.1 (2019) 43-48. https://doi.org/ 10.3846/gac.2019.7921.
 T.P. Robinson, G. Metternicht, Testing the performance of spatial interpolation techniques for mapping soil properties, Computers and electronics in agriculture 50.2 (2006) 97-108. doi:10.1016/j.compag.2005.07.003.
 D. Zimmerman, et al., An experimental comparison of ordinary and universal krigingand inverse distance weighting, Mathematical Geology 31 (1999) 375-390.
 Dennis Weber, Evan Englund, Evaluation and comparison of spatial interpolators, Mathematical Geology 24.4 (1992) 381-391.
 J Gallichand, D Marcotte, Mapping clay content for subsurface drainage in the Nile Delta, Geoderma 58.3-4 (1993) 165-179. https://doi.org/ 10.1016/0016-7061(93)90040-R.
 D.J. Brus, et al., The performance of spatial interpolation methods and choropleth maps to estimate properties at points: a soil survey case study, Environmetrics 7.1 (1996) 1-16.
 J. Fernando Aguilar, et al., Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy, Photogrammetric Engineering & Remote Sensing 71.7 (2005) 805-816.
 Qulin Tan, Xiao Xu, Comparative analysis of spatial interpolation methods: an experimental study, Sensors & Transducers 165.2 (2014) 155.
 David F Watson, A refinement of inverse distance weighted interpolation, Geoprocessing 2 (1985) 315-327.
 J. Pohjola, J. Turunen, T. Lipping, Creating High-resolution Digital Elevation Model Using Thin Plate Spline Interpolation and Monte Carlo Simulation, Working Report (2009).
 R. Sibson, A Brief Description of Nearest Neighbor Interpolation, Interpolating Multivariate Data, John Wiley & Sons, New York (1981) 21-36.