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DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning
 
 
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Correction

Correction: 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

Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(11), 724; https://doi.org/10.3390/ijgi10110724
Submission received: 27 August 2021 / Accepted: 1 October 2021 / Published: 27 October 2021
(This article belongs to the Special Issue Geo-Information for Watershed Processes)
The authors of the published paper [1] 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 FactorsNo. of FactorsAverage RMSE (mm/yr)Average NSE
TrainingTestTrainingTest
RF (all)360.96 2.01 0.83 0.25
GBM (all)360.88 1.84 0.84 0.39
RF (confirmed)161.08 1.91 0.79 0.31
GBM (confirmed)160.79 1.50 0.88 0.59
RF (nonrejected)191.09 1.96 0.79 0.27
GBM (nonrejected)190.82 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 FactorsNo. of FactorsAverage RMSE (mm/yr)Average NSE
TrainingTestTrainingTest
RF (all)360.96 2.01 0.83 0.25
GBM (all)360.88 1.84 0.84 0.39
RF (confirmed)151.08 1.91 0.79 0.31
GBM (confirmed)150.79 1.50 0.88 0.59
RF (nonrejected)161.09 1.96 0.79 0.27
GBM (nonrejected)160.82 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

  1. 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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MDPI and ACS Style

Nguyen, K.A.; Chen, W. Correction: 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. ISPRS Int. J. Geo-Inf. 2021, 10, 724. https://doi.org/10.3390/ijgi10110724

AMA Style

Nguyen KA, Chen W. Correction: 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. ISPRS International Journal of Geo-Information. 2021; 10(11):724. https://doi.org/10.3390/ijgi10110724

Chicago/Turabian Style

Nguyen, Kieu Anh, and Walter Chen. 2021. "Correction: 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" ISPRS International Journal of Geo-Information 10, no. 11: 724. https://doi.org/10.3390/ijgi10110724

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