Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/81701

TitleSoil erosion quantification using Machine Learning in sub-watersheds of Northern Portugal
Author(s)Folharini, Saulo Oliveira
Vieira, António
Bento-Gonçalves, António
Silva, Sara
Marques, Tiago Ribeiro
Novais, Jorge Leandro Ramalho
Keywordssoil erosion
sub-watersheds
machine learning
burned areas
protected areas
Issue date2023
PublisherMultidisciplinary Digital Publishing Institute (MDPI)
JournalHydrology
CitationFolharini, S.; Vieira, A.; Bento-Gonçalves, A.; Silva, S.; Marques, T.; Novais, J. Soil Erosion Quantification using Machine Learning in Sub-Watersheds of Northern Portugal. Hydrology 2023, 10, 7. https://doi.org/10.3390/hydrology10010007
Abstract(s)Protected areas (PA) play an important role in minimizing the effects of soil erosion in watersheds. This study evaluated the performance of machine learning models, specifically support vector machine with linear kernel (SVMLinear), support vector machine with polynomial kernel (SVMPoly), and random forest (RF), on identifying indicators of soil erosion in 761 sub-watersheds and PA in northern Portugal, by using soil erosion by water in Europe, according to the revised universal soil loss equation (RUSLE2015), as target variable. The parameters analyzed were: soil erosion by water in Europe according to the revised universal soil loss equation (RUSLE2015), total burned area of the sub-watershed in the period of 1975-2020, fire recurrence, topographic wetness index (TWI), and the morphometric factors, namely area (A), perimeter (P), length (L), width (W), orientation (O), elongation ratio (Re), circularity ratio (Rc), compactness coefficient (Cc), form factor (Ff), shape factor (Sf), DEM, slope, and curvature. The median coefficient of determination (R2) for each model was RF (0.61), SVMpoly (0.68), and SVMLinear (0.54). Regarding the analyzed parameters, those that registered the greatest importance were A, P, L, W, curvature, and burned area, indicating that an analysis which considers morphometric factors, together with soil erosion data affected by water and soil moisture, is an important indicator in the analysis of soil erosion in watersheds.
TypeArticle
DescriptionData Availability Statement: Soil erosion by water (RUSLE2015), available at: https://esdac.jrc.ec.europa.eu/content/soil-erosion-water-rusle2015, accessed on 21 December 2022; European Digital Elevation Model (EU-DEM), available at: https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1, accessed on 21 December 2022; Watersheds, available at: https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/search?anysnig=Bacias%20Hidrogr%C3%A1ficas%20das%20Massas%20de%20%C3%81gua%20de%20Portugal%20Continental:%20CDG%20SNIAmb&fast=index, accessed on 21 December 2022; Burned areas, available at: https://sig.icnf.pt/portal/home/item.html?id=983c4e6c4d5b4666b258a3ad5f3ea5af, accessed on 21 December 2022; Protected areas, available at: https://geocatalogo.icnf.pt/catalogo_tema1.html, accessed on 21 December 2022.
URIhttps://hdl.handle.net/1822/81701
DOI10.3390/hydrology10010007
e-ISSN2306-5338
Publisher versionhttps://www.mdpi.com/2306-5338/10/1/7
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CECS - Artigos em revistas internacionais / Articles in international journals

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