Research Article
Hybridization of Machine Learning Algorithms and an Empirical Regression Model for Predicting Debris-Flow-Endangered Areas
Table 1
Ranges of VD, H, and Lf at training and testing stages.
| Parameters | VD (×103 m3) | H (km) | Lf (km) |
| Total data | 1.2–3238.4 | 0.22–2.98 | 0.03–0.88 | Cross-validation | Training | | | CR1 | 2.7–3238.4 | 0.22–2.98 | 0.03–0.88 | CR2 | 1.2–3238.4 | 0.22–2.48 | 0.03–0.88 | CR3 | 1.2–3238.4 | 0.22–2.98 | 0.03–0.88 | CR4 | 1.2–3238.4 | 0.26–2.98 | 0.03–0.88 | CR5 | 1.2–3100.0 | 0.22–2.98 | 0.038–0.51 |
| Cross-validation | Testing | | | CR1 | 1.2–711.0 | 0.34–2.48 | 0.04–0.39 | CR2 | 3.0–561.0 | 0.36–2.98 | 0.042–0.51 | CR3 | 2.9–1034.0 | 0.26–1.60 | 0.038–0.50 | CR4 | 4.5–3100.0 | 0.22–2.02 | 0.05–0.49 | CR5 | 2.7–3238.4 | 0.26–1.98 | 0.03–0.88 |
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