Table 1.
One-step regression fitting performance (MSE, RMSE, MAE, R²) of traditional machine learning models on the training set (feature selection stage; not comparable to the multi-step forecasting evaluation in Table 2) for Northern and Southern China
| Model | Region | MSE | RMSE | MAE |
|
|---|---|---|---|---|---|
| Random Forest | Northern | 0.0018 | 0.0426 | 0.0310 | 0.8931 |
| Random Forest | Southern | 0.0038 | 0.0619 | 0.0406 | 0.7946 |
| XGBoost | Northern | 0.0020 | 0.0452 | 0.0292 | 0.8794 |
| XGBoost | Southern | 0.0036 | 0.0600 | 0.0352 | 0.8071 |
| LightGBM | Northern | 0.0016 | 0.0401 | 0.0282 | 0.9050 |
| LightGBM | Southern | 0.0030 | 0.0546 | 0.0353 | 0.8401 |