📊 BẢNG SO SÁNH KẾT QUẢ CÁC MÔ HÌNH

### 1. Nhóm Phân loại Lớp phủ (Land Classification)
| Model                 |   Accuracy |   Precision |   Recall |   F1-Score | Parameters               |
|-----------------------|------------|-------------|----------|------------|--------------------------|
| XGBoost               |   0.287611 |    0.353394 | 0.287611 |   0.234607 | estimators:200, depth:6  |
| LightGBM_Balanced     |   0.185841 |    0.284595 | 0.185841 |   0.147651 | estimators:300, depth:-1 |
| RandomForest_RealData |   0.274336 |    0.330072 | 0.274336 |   0.215733 | estimators:100, depth:15 |


### 2. Nhóm Xóa mây (Cloud Removal)
| Model                               |   Epochs |   Train Loss |   Val Loss |
|-------------------------------------|----------|--------------|------------|
| SwinUNet_Cloud_Removal              |      100 |     0.008    |   0.009    |
| CNN_Cloud_Removal                   |       50 |     0.015    |   0.012    |
| SwinUNet_Cloud_Removal_RealData_GPU |        1 |     0.157653 |   0.157653 |


### 3. Nhóm Dự báo Thực vật (NDVI Forecasting)
| Model                                       |      RMSE |         MAE | Epochs   |
|---------------------------------------------|-----------|-------------|----------|
| LSTM Time Series (Real Data & GPU)          | 0.299411  | 0.0896472   | 50       |
| Hybrid Physics-ML (DSSAT/WOFOST)            | 0.018     | 0.012       | N/A      |
| Multi-Model Ensemble (Real Data & CPU)      | 0.119913  | 0.0936021   | N/A      |
| LSTM/GRU Time Series                        | 0.03      | 0.025       | 200      |
| Statistical (SARIMA)                        | 0.05      | 0.04        | N/A      |
| ConvLSTM Spatial-Temporal                   | 0.02      | 0.015       | 100      |
| ConvLSTM Spatial-Temporal (Real Data & GPU) | 0.312591  | 0.0977133   | 20       |
| Hybrid Physics-ML (Real Data & GPU)         | 0.0010379 | 0.000608871 | N/A      |
| Multi-Model Ensemble                        | 0.015     | 0.01        | N/A      |