Table 4.
The most appropriate features for the machine learning methods determined through the trial-and-error process.
| Model | The Most Appropriate Characteristics | Collection | AAPD% | RAPE% | RMSE | R2 |
|---|---|---|---|---|---|---|
| MLPNN | Nine hidden neurons | Training | 11.13 | 7.38 | 4.95 | 0.988679 |
| Hyperbolic tangent and logistic | Testing | 6.25 | 5.37 | 2.38 | 0.997467 | |
| Levenberg optimization algorithm | Overall | 10.39 | 7.07 | 4.65 | 0.990062 | |
| CFFNN | Nine hidden neurons | Training | 8.74 | 6.68 | 4.54 | 0.990058 |
| Hyperbolic tangent and logistic | Testing | 9.42 | 7.28 | 5.32 | 0.990337 | |
| Levenberg optimization algorithm | Overall | 8.84 | 6.76 | 4.67 | 0.990082 | |
| RNN | Seven hidden neurons | Training | 10.92 | 9.81 | 4.00 | 0.992677 |
| Hyperbolic tangent and logistic | Testing | 11.07 | 13.76 | 9.14 | 0.966081 | |
| Scaled conjugate gradient algorithm | Overall | 10.94 | 10.44 | 5.12 | 0.988174 | |
| LSSVR | Gaussian kernel function | Training | 13.03 | 8.14 | 5.22 | 0.987382 |
| Testing | 14.13 | 8.78 | 4.33 | 0.992005 | ||
| Overall | 13.20 | 8.24 | 5.09 | 0.988064 | ||
| ANFIS2 | Hybrid optimization algorithm Cluster radius = 0.5 |
Training | 8.54 | 5.27 | 4.41 | 0.991163 |
| Testing | 16.28 | 8.79 | 5.36 | 0.985432 | ||
| Overall | 9.71 | 5.74 | 4.57 | 0.990414 | ||
| ANFIS3 | Hybrid optimization algorithm Nine clusters |
Training | 25.81 | 13.87 | 6.29 | 0.981923 |
| Testing | 19.01 | 18.39 | 7.78 | 0.971648 | ||
| Overall | 24.78 | 14.53 | 6.54 | 0.980306 |