Research Article
Spatial Interpolation of Annual Runoff in Ungauged Basins Based on the Improved Information Diffusion Model Using a Genetic Algorithm
Table 5
The RMSE,
,
, and MAPE of five different models in Hongqi site and Baimasi site (where boldface font indicates the best performance).
| Site | Model | RMSE | | | MAPE |
| Hongqi | IDW | 6.42 | 0.8691 | 0.7572 | 92.55% | COK | 5.32 | 0.8951 | 0.8103 | 67.32% | SIDM | 5.61 | 0.9037 | 0.8239 | 69.14% | OIDM | 5.01 | 0.9124 | 0.8311 | 53.68% | GIDM | 3.91 | 0.9251 | 0.8317 | 38.02% |
| Baimasi | IDW | 3.24 | 0.8238 | 0.7143 | 93.13% | COK | 2.91 | 0.8451 | 0.7326 | 65.28% | SIDM | 3.03 | 0.8572 | 0.7469 | 69.10% | OIDM | 2.80 | 0.8913 | 0.7905 | 44.89% | GIDM | 2.04 | 0.9116 | 0.8076 | 26.29% |
| The average | IDW | 4.83 | 0.8465 | 0.7358 | 92.84% | COK | 4.115 | 0.8701 | 0.7715 | 66.30% | SIDM | 4.32 | 0.8805 | 0.7854 | 69.12% | OIDM | 3.905 | 0.90185 | 0.8108 | 49.29% | GIDM | 2.975 | 0.9184 | 0.8197 | 32.16% |
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