GIDMGlobal Identity Management (various companies)
GIDMGlobally-Indexed Distributed Memory (computing)
GIDMGiDesign and Marketing (Redding, CA)
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Under this case, in this study area the GIDM method is used to research the spatial specificity of runoff and the RMSE, R, E, and MAPE statistics of interpolation results is 5.97 (x [10.sup.8] [m.sup.3]), 0.9260, 0.8213, and 18.42%, which is a little worse than those of Lanzhou site.
The correlation of the GIDM between reconstructed and observed runoff is best among the five different models, although some slight oversimulations and undersimulations exist.
The average MAPE of GIDM is 32.16% and the average R is above 0.9, which indicates that the general results of GIDM still could be acceptable.
For example, the average MAPE of GIDM is 40.56% in experiment 6, while that in experiment 3 is only 32.16%.
So in this experiment, the average MAPE of GIDM is 40.56% and the average R is above 0.85.
Table 9 shows a comparison among different five models and the GIDM method obtains best accuracy among five different models in terms of different evaluation measures.
(1) The GIDM model is a nonlinear information diffusion model, which is in line with the changes in the hydrological runoff.
But the calculation process of our GIDM model is simple and convenient to operate.
Seven experiments based on GIDM for the runoff interpolation at different stations on the Yellow River are carried out, compared with SIDM, OIDM, IDW, and COK.
(1) GIDM is a useful tool for the river runoff spatial interpolation.
Under this situation, the interpolation results of the GIDM method in study area are still good, which can overcome the traditional hydrological interpolation methods having good interpolating results only in the natural watershed.
(3) The previous six experiments proved that the interpolation results of the GIDM method on great rivers are good.