Journal of Spatial Analysis

In many scientific researches, error measurement statistics are often used without taking notices into account when selecting a model or method for the spatial analysis of environmental hazards. In order to assess the accuracy of precipitation interpolation methods in Fars province, the performance of widely used error measurement statistics and some comments were implemented. Spatial interpolation of precipitation was accomplished using inverse distance weighting, kriging, co-kriging, and radial basis functions methods with 161 weather stations (22 synoptic and 139 rain gauge stations) for 2018 as a rainy year. The results of MBE statistic evaluation indicated that the researcher may have chosen the incorrect interpolation method in certain cases where the sum of the positive and negative values became zero. In addition, this statistic is limited to indicating overestimation or underestimation and should not be used for assessing accuracy or selecting interpolation techniques. Regarding the coefficient of determination (r²), the results revealed that due to the lack of compatibility in the magnitude of the range of this coefficient (0 to 1) with error values (100 to 400 mm for the interpolation of precipitation in Fars province), its use in evaluation of the accuracy of a method is not recommended. In terms of NRMSE, the results showed that samples with a small number of observations (n=3), its value increased excessively (NRMSE=0.35) when compared to samples with a bigger number of data (n=20, NRMSE=0.097). Therefore, it is not advised to use this statistic. In conclusion, since MAE and RMSE statistics provide a more realistic error value, it is advised to use them for assessing the accuracy of interpolation methods.