Kieu Anh Nguyen
Biographic Data
| ID | 9819499 |
|---|---|
| NAME | Kieu Anh Nguyen |
| GIVEN NAMES | Kieu Anh |
| FAMILY NAME | Nguyen |
| SIGNATURE | NGUYEN K A |
| AFFILIATIONS | National Taipei University of Technology |
| ORCID | 0000-0003-2633-9873 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements
Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging…
DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning
Soil erosion is a form of land degradation. It is the process of moving surface soil with the action of external forces such as wind or water. Tillage also causes soil erosion. As outlined by the United Nations Sustainable Development Goal (UN SDG) #15, it is a global challenge to “combat desertification, and halt and reverse land degradation and halt biodiversity loss.” In order to advance this goal, we studied and modeled the soil erosion depth…
Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds
The universal soil loss equation (USLE) is a widely used empirical model for estimating soil loss. Among the USLE model factors, the cover management factor (C-factor) is a critical factor that substantially impacts the estimation result. Assigning C-factor values according to a land-use/land-cover (LULC) map from field surveys is a typical traditional approach. However, this approach may have limitations caused by the difficulty and cost in cond…
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Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements
Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging…
DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning
Soil erosion is a form of land degradation. It is the process of moving surface soil with the action of external forces such as wind or water. Tillage also causes soil erosion. As outlined by the United Nations Sustainable Development Goal (UN SDG) #15, it is a global challenge to “combat desertification, and halt and reverse land degradation and halt biodiversity loss.” In order to advance this goal, we studied and modeled the soil erosion depth…
Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds
The universal soil loss equation (USLE) is a widely used empirical model for estimating soil loss. Among the USLE model factors, the cover management factor (C-factor) is a critical factor that substantially impacts the estimation result. Assigning C-factor values according to a land-use/land-cover (LULC) map from field surveys is a typical traditional approach. However, this approach may have limitations caused by the difficulty and cost in cond…
Computer Science (3 works) · Machine learning (3 works) · Random forest (3 works) · Soil erosion and sediment transport (3 works) · Engineering (2 works) · Environmental Science (2 works) · Erosion (2 works) · Geography (2 works) · Geology (2 works) · Groundwater and Watershed Analysis (2 works)