Research Article
Prediction of the Reference Evapotranspiration Using a Chaotic Approach
Table 4
Prediction results for daily reference evapotranspiration of the stations selected in China by chaotic model, AR model, and BP neural network model.
| | Location | Mean absolute error (MAE) | Root mean square error (RMSE) | Correlation coefficient (CC) | Modified coefficient of efficiency | Optimal embedding dimension (m) | Optimal number of neighbors |
| Chaotic Model | Baotou | 0.1378 | 0.1999 | 0.9965 | 0.913 | 4 | 72 | Zhangbei | 0.2023 | 0.2799 | 0.9887 | 0.8709 | 4 | 76 | Kaifeng | 0.1962 | 0.3095 | 0.9957 | 0.8679 | 4 | 80 | Shaoguan | 0.2358 | 0.3054 | 0.9784 | 0.8078 | 4 | 68 |
| BP Model | Baotou | 0.203 | 0.098 | 0.992 | 0.872 | | | Zhangbei | 0.189 | 0.089 | 0.989 | 0.894 | | | Kaifeng | 0.198 | 0.076 | 0.997 | 0.871 | | | Shaoguan | 0.209 | 0.082 | 0.993 | 0.834 | | |
| AR Model | Baotou | 0.573 | 0.665 | 0.876 | 0.652 | | | Zhangbei | 0.612 | 0.672 | 0.889 | 0.673 | | | Kaifeng | 0.553 | 0.589 | 0.897 | 0.709 | | | Shaoguan | 0.557 | 0.699 | 0.903 | 0.655 | | |
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