DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning
Bibliographic Data
| ID | 22032162 |
|---|---|
| Authors | Kieu Anh Nguyen (0000-0003-2633-9873, National Taipei University of Technology), Walter Chen (0000-0001-6926-0837, National Taipei University of Technology, corresponding author) |
| Year | 2021 |
| Volume | 10 |
| Issue | 7 |
| Pages | 452 |
| Publication date | 2021-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10070452 |
| OpenAlex | W3176554196 |
| Language | EN |
| References cited | 37 |
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 of a typical watershed in Taiwan using 26 morphometric factors derived from a digital elevation model (DEM) and 10 environmental factors. Feature selection was performed using the Boruta algorithm to determine 15 factors with confirmed importance and one tentative factor. Then, machine learning models, including the random forest (RF) and gradient boosting machine (GBM), were used to create prediction models validated by erosion pin measurements. The results show that GBM, coupled with 15 important factors (confirmed), achieved the best result in the context of root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE). Finally, we present the maps of soil erosion depth using the two machine learning models. The maps are useful for conservation planning and mitigating future soil erosion
Agriculture · Civil engineering · Desertification · Erosion · Geography · Geotechnical engineering · Land degradation · Land use · Machine learning · Random forest · Soil conservation · Soil retrogression and degradation · Soil water · Surface runoff · Tillage · Watershed · WEPP · Computer Science · Engineering · Environmental Science · Groundwater and Watershed Analysis · Hydrology and Watershed Management Studies · Soil erosion and sediment transport · Geology · Soil Science
Erosional Development of Streams and Their Drainage Basins; Hydrophysical Approach to Quantitative Morphology
Evolution of Drainage Systems and Slopes in Badlands at Perth Amboy, New Jersey
Feature Selection with the Boruta Package
The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals
Greedy function approximation
Random Forests
Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements
Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds
An Analysis of the Relations Among Elements of Climate, Surface Properties, and Geomorphology
| Highly cited | No |
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