Table 2.
Results of the conventional machine learning models and the deep neural network on the OFDF training, validation and test sets.
| Machine learning technique | Training set |
Validation set |
Test set |
||||||
|---|---|---|---|---|---|---|---|---|---|
| Accuracy (%) | RMSE | MAE | Accuracy (%) | RMSE | MAE | Accuracy (%) | RMSE | MAE | |
| MLR | 90.11 | 0.0671 | 0.0508 | 60.00 | 0.1311 | 0.0999 | 65.00 | 0.1778 | 0.1183 |
| PLSR | 76.92 | 0.0917 | 0.0705 | 70.00 | 0.1136 | 0.0835 | 70.00 | 0.0970 | 0.0705 |
| SVM | 79.12 | 0.1136 | 0.0711 | 70.00 | 0.1308 | 0.0959 | 75.00 | 0.1039 | 0.0795 |
| ANN | 74.73 | 0.1140 | 0.0809 | 70.00 | 0.1105 | 0.0846 | 70.00 | 0.0959 | 0.0772 |
| RF | 84.62 | 0.0775 | 0.0567 | 80.00 | 0.0917 | 0.0721 | 70.00 | 0.1068 | 0.0774 |
| k-NN | 80.22 | 0.0975 | 0.0649 | 75.00 | 0.1025 | 0.0727 | 75.00 | 0.0877 | 0.0608 |
| DNN | 97.80 | 0.0420 | 0.0307 | 80.00 | 0.0842 | 0.0705 | 80.00 | 0.0714 | 0.0565 |