The authors of the published paper [] would like to make the following corrections:
- (1)
- The last four numbers in the second column (No. of Factors) of Table 3 should read 15, 15, 16, and 16 (instead of 16, 16, 19, and 19)
Original:
Table 3.
Performance comparison of machine learning models using threefold cross-validation.
Table 3.
Performance comparison of machine learning models using threefold cross-validation.
| Model and Factors | No. of Factors | Average RMSE (mm/yr) | Average NSE | ||
|---|---|---|---|---|---|
| Training | Test | Training | Test | ||
| RF (all) | 36 | 0.96 | 2.01 | 0.83 | 0.25 |
| GBM (all) | 36 | 0.88 | 1.84 | 0.84 | 0.39 |
| RF (confirmed) | 16 | 1.08 | 1.91 | 0.79 | 0.31 |
| GBM (confirmed) | 16 | 0.79 | 1.50 | 0.88 | 0.59 |
| RF (nonrejected) | 19 | 1.09 | 1.96 | 0.79 | 0.27 |
| GBM (nonrejected) | 19 | 0.82 | 1.52 | 0.87 | 0.57 |
Corrected:
Table 3.
Performance comparison of machine learning models using threefold cross-validation.
Table 3.
Performance comparison of machine learning models using threefold cross-validation.
| Model and Factors | No. of Factors | Average RMSE (mm/yr) | Average NSE | ||
|---|---|---|---|---|---|
| Training | Test | Training | Test | ||
| RF (all) | 36 | 0.96 | 2.01 | 0.83 | 0.25 |
| GBM (all) | 36 | 0.88 | 1.84 | 0.84 | 0.39 |
| RF (confirmed) | 15 | 1.08 | 1.91 | 0.79 | 0.31 |
| GBM (confirmed) | 15 | 0.79 | 1.50 | 0.88 | 0.59 |
| RF (nonrejected) | 16 | 1.09 | 1.96 | 0.79 | 0.27 |
| GBM (nonrejected) | 16 | 0.82 | 1.52 | 0.87 | 0.57 |
- (2)
- The last parentheses on page 13 (4.3 Model Prediction) should contain the numbers 15 and 16 (instead of 16 and 19)
Original:
They both have a similar appearance because they used a similar number of variables (16 and 19).
Corrected:
They both have a similar appearance because they used a similar number of variables (15 and 16).
The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original article has been updated.
Reference
- Nguyen, K.A.; Chen, W. DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning. ISPRS Int. J. Geo-Inf. 2021, 10, 452. [Google Scholar] [CrossRef]
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