A Strategy to Optimize the Implementation of a Machine-Learning Scheme for Extreme Meiyu Rainfall Prediction over Southern Taiwan
Abstract
:1. Introduction
2. Data and Methodology
2.1. Predictand and Predictors
2.2. The SVM-Based Prediction Scheme
2.3. Experiments
2.3.1. Choice of Predictors
2.3.2. Domain Selection
2.4. Evaluation Methods
- ACC depicts the level of agreement between the result of identification and observation. The lower accuracy would be 0, and the higher accuracy would be 1.
- PPV demonstrates the ability of schemes in identifying cases of a true positive. The formula can be written as:
- POD is also known as the true positive rate. It measures the portion of hits that are correctly identified:
- F1-score is a common measurement for anomaly detection. A higher weighting is given in the F1-score for true positive cases. The mathematic form of F1-score can be expressed as:
3. Results
4. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variables | Group1 | Group2 |
---|---|---|
Mean sea level pressure (MSLP) | Surface | Surface |
Height (H) | 100–950 hPa, interval 50 hPa | 700 hPa, 850 hPa |
Temperature (T) | 100–1000 hPa, interval 50 hPa | 700 hPa, 850 hPa, 925 hPa, 1000 hPa |
Relative humidity (RH) | 100–1000 hPa, interval 50 hPa | 700 hPa, 850 hPa, 925 hPa, 1000 hPa |
Zonal wind (U) | 100–1000 hPa, interval 50 hPa | 200 hPa, 400 hPa, 700 hPa, 850 hPa, 1000 hPa |
Meridional wind (V) | 100–1000 hPa, interval 50 hPa | 200 hPa, 400 hPa, 700 hPa, 850 hPa, 1000 hPa |
Vertical velocity (W) | 100–1000 hPa, interval 50 hPa |
Experiments | Variable Type | Domain Type | Lead Time |
---|---|---|---|
EXP-G1D1 | Group1 | Domain-1 | 16 h |
EXP-G2D1 | Group2 | Domain-1 | 16 h |
EXP-G2D2 | Group2 | Domain-2 | 16 h |
EXP-G2D2-L2 | Group2 | Domain-2 | 28 h |
EXP-G2D2-L3 | Group2 | Domain-2 | 40 h |
EXP-G2D2-L4 | Group2 | Domain-2 | 52 h |
EXP-G2D2-L5 | Group2 | Domain-2 | 64 h |
EXP-G2D2-L6 | Group2 | Domain-2 | 76 h |
Observation | |||
---|---|---|---|
True | False | ||
Predict | True | A (true positive) | B (false positive) |
False | C (false negative) | D (true negative) |
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Chu, J.-L.; Chiang, C.-C.; Hsu, L.-H.; Hwang, L.-R.; Yu, Y.-C.; Lin, K.-L.; Wang, C.-J.; Su, S.-H.; Yo, T.-S. A Strategy to Optimize the Implementation of a Machine-Learning Scheme for Extreme Meiyu Rainfall Prediction over Southern Taiwan. Water 2021, 13, 2884. https://doi.org/10.3390/w13202884
Chu J-L, Chiang C-C, Hsu L-H, Hwang L-R, Yu Y-C, Lin K-L, Wang C-J, Su S-H, Yo T-S. A Strategy to Optimize the Implementation of a Machine-Learning Scheme for Extreme Meiyu Rainfall Prediction over Southern Taiwan. Water. 2021; 13(20):2884. https://doi.org/10.3390/w13202884
Chicago/Turabian StyleChu, Jung-Lien, Chou-Chun Chiang, Li-Huan Hsu, Li-Rung Hwang, Yi-Chiang Yu, Kuan-Ling Lin, Chieh-Ju Wang, Shih-Hao Su, and Ting-Shuo Yo. 2021. "A Strategy to Optimize the Implementation of a Machine-Learning Scheme for Extreme Meiyu Rainfall Prediction over Southern Taiwan" Water 13, no. 20: 2884. https://doi.org/10.3390/w13202884
APA StyleChu, J.-L., Chiang, C.-C., Hsu, L.-H., Hwang, L.-R., Yu, Y.-C., Lin, K.-L., Wang, C.-J., Su, S.-H., & Yo, T.-S. (2021). A Strategy to Optimize the Implementation of a Machine-Learning Scheme for Extreme Meiyu Rainfall Prediction over Southern Taiwan. Water, 13(20), 2884. https://doi.org/10.3390/w13202884