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Showing 2 results for Interpolation Methods

S.h. Tabatabaei, M. Ghazali ,
Volume 15, Issue 57 (10-2011)
Abstract

The accuracy and precision of the input data in decision making is important. Error originates from data collection, data entry, storage, retrieval and analysis of the data which consequently result in model error. One of the errors in spatial analysis is interpolation error. The main objective of this research was the suitability assessment of some interpolation methods for estimation of groundwater level in Farsan-Joneghan and Sefiddasht aquifers, located in Beheshtabad catchment, Chaharmahl-Va-Bakhtiyari province, Iran. Cross-validation technique was employed for the determination of each method's error. The RMSE and MAE indices were used for the error comparison. The results show that the modified Shapard's method with an MAE=6 and RMSE=7 was the most accurate for interpolation of groundwtaer level in the Sefiddasht aquifer. The inverse distance power method with MAE=6 and RMSE=9 was the best interpolation method for Farsan-Jonaghan aquifer. The Kriging with MAE=7 and RMSE=12 is the second best method in these aquifers. The moving average, minimum curvature and polynomial regression procedures produce the maximum error in the aquifers (17
M. A. Amini, G. Torkan, S. S. Eslamian, M. J. Zareian, A. A. Besalatpour,
Volume 23, Issue 1 (6-2019)
Abstract

In the present study, we used 27 precipitation average monthly data from synoptic, climatologic, rain-guage and evaporative stations located in Zayandeh-Rud river basin for the period of 1970-2014. Before interpolating, the missing data in the time series of each station was reconstructed by the normal ratio method. Also, for the data quality control, the Dickey-Fuller and Shapiro-Wilk tests were used to check the data stationarity and normality. Then, these data were interpolated by six interpolation methods including   Inverse Distance Weighting, Natural Neighbor, Tension Spline, Regularized Spline, Ordinary Kriging and Universal Kriging; then each method was evaluated using the cross-validation technique with MAE, MBE and RMSE indices. The results showed that among the spatial interpolation methods, Natural Neighbor method with MAE of 0.24 had the best performance for interpolating precipitation among all of the methods. Also, among Ordinary Kriging, Universal Kriging, Spline and Inverse Distance Weighting methods, respectively, Exponential Kriging with MAE 0.54, Quadratic Drift Kriging with MAE of 0.5, Tension Spline with the MAE of 0.54 and Inverse Distance Weighting with the power of 4 with MAE of 0.57 had the least error compared to other IDW methods.


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