Research Article
Peak Ground Acceleration Prediction by Artificial Neural Networks for Northwestern Turkey
Table 6
R, RMSE, and MAE values of each ANN method for
models (v), (vi), (vii), (viii), and (ix) in the test period.
| Model | Inputs | Outputs | FFBP method | GRNN method | RBF method | R | RMSE (cm/s/s) | MAE (cm/s/s) | R | RMSE (cm/s/s) | MAE (cm/s/s) | R | RMSE (cm/s/s) | MAE (cm/s/s) |
| (v) | , FD and HD | PGA for E-W | 0.856 | 44.45 | 18.46 | 0.829 | 46.89 | 18.63 | 0.487 | 55.68 | 29.79 | (vi) | , FD and HD | PGA for N-S | 0.122 | 33.22 | 16.43 | 0.109 | 32.95 | 16.62 | 0.066 | 47.27 | 26.73 | (vii) | , FD and HD | PGA for U-D | 0.093 | 98.38 | 93.61 | 0.933 | 14.22 | 5.27 | 0.751 | 21.87 | 10.16 | (viii) | , FD and HD | Max. PGA | 0.895 | 29.55 | 15.18 | 0.882 | 45.29 | 19.38 | 0.563 | 61.52 | 37.75 | (ix) | , FD, HD and SC | Max. PGA | 0.667 | 46.27 | 21.94 | 0.803 | 36.56 | 15.35 | 0.07 | 50.48 | 95.93 |
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