Precipitation Estimation on Kyushu (Japan) regional dataset 2022
1.119RMSE (r)NSP
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| NSP2026.04 | 1.119 | 0.701 | 0.485 | 0.155 | 0.553 | |
| IDW2026.04 | 1.272 | 0.753 | 0.68 | 0.245 | 0.397 | |
| GSMaP GC2026.04 | 1.288 | 0.808 | 0.762 | 0.27 | 0.265 | |
| U-Net2026.04 | 1.324 | 0.726 | 0.679 | 0.186 | 0.532 | |
| ConvCNP2026.04 | 1.363 | 0.752 | 0.645 | 0.17 | 0.525 | |
| XGBoost2026.04 | 1.501 | 0.835 | 0.985 | 0.404 | 0.19 | |
| GSMaP2026.04 | 1.508 | 0.882 | 0.965 | 0.326 | 0.158 | |
| Kriging2026.04 | 1.64 | 0.936 | 0.973 | 0.364 | 0.114 | |
| Linear regression2026.04 | 1.688 | 0.831 | 0.768 | 0.275 | 0.356 | |
| Cokriging2026.04 | 1.715 | 0.951 | 1.077 | 0.384 | 0.139 | |
| EMOS2026.04 | 1.738 | 0.954 | 1.082 | 0.326 | 0.169 | |
| CNP2026.04 | 1.779 | 1.559 | 1.953 | 1.815 | 0.437 | |
| GWR2026.04 | 1.788 | 0.812 | 0.655 | 0.252 | 0.389 | |
| Quantile mapping2026.04 | 1.876 | 1.073 | 1.377 | 0.623 | 0.216 | |
| ViT2026.04 | 2.979 | 2.01 | 1.993 | 1.275 | 0.483 |