| S. No | Methods | Assumptions | Limitations | Suitability |
| 1 | NN | -Best local predictor is nearest data point -Relatively the simplest method | -No error assessment, only one data point per polygon. | -Nominal data from observations |
| 2 | IDWA | -Underlying surface is smooth | No error assessment and results depend on size of search window and choice of weighting parameter | -Quick interpolation from sparse data on regular grid or irregular spaced samples |
| 3 | MIDWA | -Can accommodate the effect of topographic variation [20] | -Similar to IDWA | -Similar to IDWA |
| 4 | KM | -Interpolated surface is smooth. Statistical stationary and the intrinsic Hypothesis. | -Error assessment depends on variogram and distribution of data points and size of interpolated blocks and requires care when modelling spatial correlation structures. | -When data are sufficient to compute variograms, kriging provides a good interpolator for sparse data. |
| 5 | TPS | -Underlying surface is smooth everywhere | -Goodness of fit possible, but within the assumptions that the fitted surface is perfectly smooth | -Quick interpolation (univariate or rmultivariate) of digital elevation data and related attributes to create digital elevation models(DEM) from moderately detailed data |