Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selection
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/17204 |
Resumo: | Gully 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. |
id |
RCAP_5511ade3bdd470bc0dbb4b0e43c30c9d |
---|---|
oai_identifier_str |
oai:sapientia.ualg.pt:10400.1/17204 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selectionAvaliação de suscetibilidade de erosão gully na bacia hidrográfica de Kondoran usando algoritmos de aprendizagem de máquina e a seleção de recursos borutaEnsemble modelingData miningGully erosionWatershed managementLand useGully 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.PTDC/GES-URB/31928/2017MDPISapientiaAhmadpour, HamedBazrafshan, OmmolbaninRafiei-Sardooi, ElhamZamani, HosseinPanagopoulos, Thomas2021-10-07T19:40:27Z2021-092021-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/17204eng10.3390/su1318101102071-1050info: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:RCAAP2023-07-24T10:29:20Zoai:sapientia.ualg.pt:10400.1/17204Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:07:13.672746Repositório 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 |
Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selection Avaliação de suscetibilidade de erosão gully na bacia hidrográfica de Kondoran usando algoritmos de aprendizagem de máquina e a seleção de recursos boruta |
title |
Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selection |
spellingShingle |
Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selection Ahmadpour, Hamed Ensemble modeling Data mining Gully erosion Watershed management Land use |
title_short |
Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selection |
title_full |
Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selection |
title_fullStr |
Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selection |
title_full_unstemmed |
Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selection |
title_sort |
Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selection |
author |
Ahmadpour, Hamed |
author_facet |
Ahmadpour, Hamed Bazrafshan, Ommolbanin Rafiei-Sardooi, Elham Zamani, Hossein Panagopoulos, Thomas |
author_role |
author |
author2 |
Bazrafshan, Ommolbanin Rafiei-Sardooi, Elham Zamani, Hossein Panagopoulos, Thomas |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Ahmadpour, Hamed Bazrafshan, Ommolbanin Rafiei-Sardooi, Elham Zamani, Hossein Panagopoulos, Thomas |
dc.subject.por.fl_str_mv |
Ensemble modeling Data mining Gully erosion Watershed management Land use |
topic |
Ensemble modeling Data mining Gully erosion Watershed management Land use |
description |
Gully 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10-07T19:40:27Z 2021-09 2021-09-01T00:00:00Z |
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 |
http://hdl.handle.net/10400.1/17204 |
url |
http://hdl.handle.net/10400.1/17204 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.3390/su131810110 2071-1050 |
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 |
MDPI |
publisher.none.fl_str_mv |
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 |
|
_version_ |
1799133316621795328 |