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Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selection

2021-09Journal article Open access
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dc.contributor.authorAhmadpour, Hamed
dc.contributor.authorBazrafshan, Ommolbanin
dc.contributor.authorRafiei-Sardooi, Elham
dc.contributor.authorZamani, Hossein
dc.contributor.authorPanagopoulos, Thomas
dc.date.accessioned2021-10-07T19:40:27Z
dc.date.available2021-10-07T19:40:27Z
dc.date.issued2021-09
dc.description.abstractGully erosion susceptibility mapping is an essential land management tool to reduce soil erosion damages. This study investigates gully susceptibility based on multiple diagnostic analysis, support vector machine and random forest algorithms, and also a combination of these models, namely the ensemble model. Thus, a gully susceptibility map in the Kondoran watershed of Iran was generated by applying these models on the occurrence and non-occurrence points (as the target variable) and several predictors (slope, aspect, elevation, topographic wetness index, drainage density, plan curvature, distance to streams, lithology, soil texture and land use). The Boruta algorithm was used to select the most effective variables in modeling gully erosion susceptibility. The area under the receiver operating characteristic curve (AUC), the receiver operating characteristics, and true skill statistics (TSS) were used to assess the model performance. The results indicated that the ensemble model had the best performance (AUC = 0.982, TSS = 0.93) compared to the others. The most effective factors in gully erosion susceptibility mapping of the study region were topological, anthropogenic, and geological. The methodology and variables of this study can be used in other regions to control and mitigate the gully erosion phenomenon by applying biophilic and regenerative techniques at the locations of the most influential factors.pt_PT
dc.description.sponsorshipPTDC/GES-URB/31928/2017pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/su131810110pt_PT
dc.identifier.eissn2071-1050
dc.identifier.urihttp://hdl.handle.net/10400.1/17204
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationImproving life in a changing urban environment through Biophilic Design (BIODES)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectEnsemble modelingpt_PT
dc.subjectData miningpt_PT
dc.subjectGully erosionpt_PT
dc.subjectWatershed managementpt_PT
dc.subjectLand usept_PT
dc.titleGully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selectionpt_PT
dc.title.alternativeAvaliação de suscetibilidade de erosão gully na bacia hidrográfica de Kondoran usando algoritmos de aprendizagem de máquina e a seleção de recursos borutapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleImproving life in a changing urban environment through Biophilic Design (BIODES)
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FGES-URB%2F31928%2F2017/PT
oaire.citation.issue18pt_PT
oaire.citation.startPage10110pt_PT
oaire.citation.titleSustainabilitypt_PT
oaire.citation.volume13pt_PT
oaire.fundingStream3599-PPCDT
person.familyNamePanagopoulos
person.givenNameThomas
person.identifierR-000-K9N
person.identifier.ciencia-id411D-5652-57A8
person.identifier.orcid0000-0002-8073-2097
person.identifier.ridA-3048-2012
person.identifier.scopus-author-id9736690000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication3dfd5be1-8e22-4dda-bd34-f3b1e5f249e2
relation.isAuthorOfPublication.latestForDiscovery3dfd5be1-8e22-4dda-bd34-f3b1e5f249e2
relation.isProjectOfPublicationb7cdcedd-843e-4dbd-825f-c451406a1ce5
relation.isProjectOfPublication.latestForDiscoveryb7cdcedd-843e-4dbd-825f-c451406a1ce5

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