{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:45:24Z","timestamp":1775738724348,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 109-2121-M-027-001"],"award-info":[{"award-number":["MOST 109-2121-M-027-001"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["IJGI"],"abstract":"<jats:p>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 \u201ccombat desertification, and halt and reverse land degradation and halt biodiversity loss.\u201d 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\u2013Sutcliffe 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.<\/jats:p>","DOI":"10.3390\/ijgi10070452","type":"journal-article","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T05:00:10Z","timestamp":1625115610000},"page":"452","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2633-9873","authenticated-orcid":false,"given":"Kieu","family":"Nguyen","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6926-0837","authenticated-orcid":false,"given":"Walter","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.5194\/soil-2-111-2016","article-title":"The significance of soils and soil science towards realization of the United Nations sustainable development goals","volume":"2","author":"Keesstra","year":"2016","journal-title":"Soil"},{"key":"ref_2","unstructured":"Bridges, E.M., Hannam, I.D., Oldeman, L.R., Pening de Vries, F.W.T., Scherr, S.J., and Sompatpanit, S. 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