Research Article
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method
Table 7
The results of compare forecasting models under 10-folds cross-validation after variable selection.
| | Methods | Index | RBF Network | Kstar | Random Forest | IBK | Random Tree |
| After variable selection | Delete the rows with missing data | CC | 0.103 | 0.665 | 0.737 | 0.233 | 0.529 | MAE | 0.181 | 0.115 | 0.108 | 0.193 | 0.143 | RMSE | 0.226 | 0.171 | 0.154 | 0.282 | 0.223 | RAE | 0.984 | 0.627 | 0.589 | 1.047 | 0.774 | RRSE | 0.994 | 0.749 | 0.677 | 1.238 | 0.977 | Series mean | CC | 0.081 | 0.688 | 0.751 | 0.295 | 0.547 | MAE | 0.169 | 0.103 | 0.098 | 0.170 | 0.131 | RMSE | 0.217 | 0.158 | 0.144 | 0.260 | 0.209 | RAE | 0.988 | 0.600 | 0.571 | 0.990 | 0.767 | RRSE | 0.996 | 0.727 | 0.661 | 1.193 | 0.960 | Linear | CC | 0.081 | 0.692 | 0.750 | 0.286 | 0.551 | MAE | 0.171 | 0.102 | 0.098 | 0.169 | 0.128 | RMSE | 0.218 | 0.158 | 0.145 | 0.261 | 0.207 | RAE | 0.988 | 0.590 | 0.566 | 0.981 | 0.740 | RRSE | 0.996 | 0.723 | 0.662 | 1.196 | 0.948 | Median of nearby points | CC | 0.083 | 0.692 | 0.752 | 0.305 | 0.555 | MAE | 0.171 | 0.102 | 0.097 | 0.169 | 0.126 | RMSE | 0.218 | 0.158 | 0.144 | 0.259 | 0.208 | RAE | 0.987 | 0.593 | 0.563 | 0.980 | 0.732 | RRSE | 0.996 | 0.722 | 0.660 | 1.186 | 0.951 | Mean of nearby points | CC | 0.082 | 0.694 | 0.753 | 0.276 | 0.537 | MAE | 0.171 | 0.102 | 0.097 | 0.171 | 0.129 | RMSE | 0.218 | 0.157 | 0.144 | 0.263 | 0.210 | RAE | 0.988 | 0.593 | 0.564 | 0.993 | 0.747 | RRSE | 0.996 | 0.721 | 0.659 | 1.204 | 0.960 | Regression | CC | 0.081 | 0.690 | 0.753 | 0.295 | 0.572 | MAE | 0.169 | 0.102 | 0.098 | 0.169 | 0.126 | RMSE | 0.217 | 0.158 | 0.144 | 0.259 | 0.204 | RAE | 0.988 | 0.595 | 0.571 | 0.989 | 0.735 | RRSE | 0.996 | 0.725 | 0.659 | 1.193 | 0.938 |
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denotes after variable selection with enhancing performance; denotes the best performance among 5 models after variable selection.
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