Table 5 Irradiance evaluation parameters of models.
From: Modeling freshwater yield: deep learning applications in seawater greenhouses in Iran
Model | MAE | \(\:{\mathbf{R}}^{2}\) | RMSE | MSE | NRMSE | |||||
|---|---|---|---|---|---|---|---|---|---|---|
Train | Test | Train | Test | Train | Test | Train | Test | Train | Test | |
BiGRU | 0.0372 | 0.0381 | 96.97 | 96.82 | 0.0024 | 0.0025 | 0.0024 | 0.0025 | 0.0024 | 0.0025 |
BiLSTM | 0.0406 | 0.0422 | 96.62 | 96.32 | 0.0026 | 0.0029 | 0.0026 | 0.0029 | 0.0026 | 0.0029 |
CNN-GRU | 0.0434 | 0.0465 | 96.18 | 95.60 | 0.0030 | 0.0034 | 0.0030 | 0.0034 | 0.0030 | 0.0034 |
CNN-LSTM | 0.0363 | 0.0381 | 97.27 | 96.97 | 0.0021 | 0.0023 | 0.0022 | 0.0023 | 0.0020 | 0.0028 |
MLP | 0.0376 | 0.0396 | 97.07 | 96.78 | 0.0023 | 0.0025 | 0.0031 | 0.0025 | 0.0023 | 0.0025 |