DEM must have a high accuracy against the actual topographic model. A model which can compute heights responding to random plane position by using of the topographic data and interpolation must be constructed. Interpolation affected by the accuraccy of the observations included noise, which affected by the slop and curvature weight. Data smoothing is a method to reduce the noise. Average declination and area ratio are variable which result similarity in according to slope. But in local area, area ratio well shows a local change. This study try to classify the terrain by the declination to analysis the effects of the declination and curvature weights, and then to represent the most probable model. The result are following : In terrain classification by the slop, p16 and p24 were fitted in the plane surface fit p16 and S in the varying surface, and S and p24 in the irregular surface in classification by curvature, p24 and S were fitted in the plane or varying surface, and p16 in the irregular surface In case of hybrid, p16, p24 and S are fitted in the plane, varying and irregular surface respectively. Smoothing is the most effective in case of slope of 50 persentage and of curvature weight of 0.0015.