Table 6 The performance evaluation for forecasting daily streamflow using LSTM.
From: Integrating numerical models with deep learning techniques for flood risk assessment
Criteria Model | Training | Testing | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | NSE | KGE | MBE | R2 | RMSE | NSE | KGE | MBE | MAE | R2 | |
MD-1 | 3.51 | 16.46 | 0.62 | 0.79 | 0.03 | 0.72 | 12.25 | 0.67 | 0.84 | 0.01 | 2.80 | 0.71 |
MD-2 | 3.25 | 16.30 | 0.66 | 0.81 | -0.49 | 0.73 | 11.89 | 0.71 | 0.84 | -0.51 | 2.59 | 0.74 |
MD-3 | 3.64 | 16.33 | 0.69 | 0.81 | 1.7 | 0.73 | 11.90 | 0.65 | 0.81 | 1.85 | 3.11 | 0.73 |
MD-4 | 4.01 | 16.38 | 0.56 | 0.74 | 1.31 | 0.72 | 11.59 | 0.64 | 0.70 | 0.97 | 3.85 | 0.73 |
MD-5 | 4.00 | 16.34 | 0.55 | 0.73 | 0.17 | 0.73 | 13.98 | 0.65 | 0.74 | 0.97 | 3.49 | 0.63 |
MD-6 | 5.46 | 16.22 | 0.63 | 0.79 | 0.27 | 0.72 | 11.7 | 0.78 | 0.89 | 0.07 | 5.29 | 0.72 |
MD-7 | 3.55 | 17.74 | 0.61 | 0.66 | 4.04 | 0.73 | 12.29 | 0.65 | 0.68 | 3.88 | 3.14 | 0.73 |
MD-8 | 2.56 | 4.57 | 0.98 | 0.94 | 0.17 | 0.98 | 6.40 | 0.89 | 0.87 | 0.09 | 3.81 | 0.92 |
MD-9 | 2.37 | 4.36 | 0.98 | 0.90 | 1.37 | 0.98 | 7.39 | 0.86 | 0.88 | 1.19 | 7.4 | 0.86 |
