Soil erosion quantification using Machine Learning in sub-watersheds of Northern Portugal

Detalhes bibliográficos
Autor(a) principal: Folharini, Saulo Oliveira
Data de Publicação: 2023
Outros Autores: Vieira, António, Bento-Gonçalves, António, Silva, Sara, Marques, Tiago Ribeiro, Novais, Jorge Leandro Ramalho
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/81701
Resumo: Data 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.
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spelling Soil erosion quantification using Machine Learning in sub-watersheds of Northern Portugalsoil erosionsub-watershedsmachine learningburned areasprotected areasCiências Naturais::Ciências da Terra e do AmbienteScience & TechnologyProteger a vida terrestreData 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.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.This research was funded by the European Regional Development Fund. Climate Change Resilient Tourism in Protected Areas of Northern Portugal (CLICTOUR - Project NORTE-01-0145-FEDER-000079).Multidisciplinary Digital Publishing Institute (MDPI)Universidade do MinhoFolharini, Saulo OliveiraVieira, AntónioBento-Gonçalves, AntónioSilva, SaraMarques, Tiago RibeiroNovais, Jorge Leandro Ramalho20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/81701engFolharini, 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/hydrology100100072306-533810.3390/hydrology100100077https://www.mdpi.com/2306-5338/10/1/7info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-23T01:32:37Zoai:repositorium.sdum.uminho.pt:1822/81701Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:26:21.586243Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Soil erosion quantification using Machine Learning in sub-watersheds of Northern Portugal
title Soil erosion quantification using Machine Learning in sub-watersheds of Northern Portugal
spellingShingle Soil erosion quantification using Machine Learning in sub-watersheds of Northern Portugal
Folharini, Saulo Oliveira
soil erosion
sub-watersheds
machine learning
burned areas
protected areas
Ciências Naturais::Ciências da Terra e do Ambiente
Science & Technology
Proteger a vida terrestre
title_short Soil erosion quantification using Machine Learning in sub-watersheds of Northern Portugal
title_full Soil erosion quantification using Machine Learning in sub-watersheds of Northern Portugal
title_fullStr Soil erosion quantification using Machine Learning in sub-watersheds of Northern Portugal
title_full_unstemmed Soil erosion quantification using Machine Learning in sub-watersheds of Northern Portugal
title_sort Soil erosion quantification using Machine Learning in sub-watersheds of Northern Portugal
author Folharini, Saulo Oliveira
author_facet Folharini, Saulo Oliveira
Vieira, António
Bento-Gonçalves, António
Silva, Sara
Marques, Tiago Ribeiro
Novais, Jorge Leandro Ramalho
author_role author
author2 Vieira, António
Bento-Gonçalves, António
Silva, Sara
Marques, Tiago Ribeiro
Novais, Jorge Leandro Ramalho
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Folharini, Saulo Oliveira
Vieira, António
Bento-Gonçalves, António
Silva, Sara
Marques, Tiago Ribeiro
Novais, Jorge Leandro Ramalho
dc.subject.por.fl_str_mv soil erosion
sub-watersheds
machine learning
burned areas
protected areas
Ciências Naturais::Ciências da Terra e do Ambiente
Science & Technology
Proteger a vida terrestre
topic soil erosion
sub-watersheds
machine learning
burned areas
protected areas
Ciências Naturais::Ciências da Terra e do Ambiente
Science & Technology
Proteger a vida terrestre
description Data 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.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/81701
url https://hdl.handle.net/1822/81701
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Folharini, 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
2306-5338
10.3390/hydrology10010007
7
https://www.mdpi.com/2306-5338/10/1/7
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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