Publication
Comparison of LiDAR- and UAV-derived data for landslide susceptibility mapping using Random Forest algorithm
| dc.contributor.author | França Pereira, Felicia | |
| dc.contributor.author | Sussel Gonçalves Mendes, Tatiana | |
| dc.contributor.author | Simoes, Silvio | |
| dc.contributor.author | Roberto Magalhães de Andrade, Márcio | |
| dc.contributor.author | Luiz Lopes Reiss, Mário | |
| dc.contributor.author | Fortes Cavalcante Renk, Jennifer | |
| dc.contributor.author | Correia da Silva Santos, Tatiany | |
| dc.date.accessioned | 2023-02-28T15:47:35Z | |
| dc.date.available | 2023-02-28T15:47:35Z | |
| dc.date.issued | 2023-01 | |
| dc.description.abstract | Earthquakes, extreme rainfall, or human activity can all cause landslides. Several landslides occur each year around the world, often resulting in casualties and economic consequences. Landslide susceptibility mapping is considered to be the main technique for predicting the likelihood of an event based on the characteristics of the physical environment. Digital Terrain Model (DTM) is one of the fundamental data of modeling and is used to derive important conditional factors for detailed scale landslide susceptibility analyses. With this in mind, this study aimed to compare landslide susceptibility maps generated by Random Forest (RF) machine learning algorithm with data from Light Detection and Range (LiDAR) and Unmanned Aerial Vehicle (UAV). To this end, the performance achieved in prediction was evaluated using statistical evaluation measures based on training and validation datasets. The obtained results showed that the accuracy of both models is greater than 0.70, the area under the curve (AUC) is greater than 0.80, and the model generated from the LiDAR data is more accurate. The results also showed that the data from UAV have potential to use in landslide susceptibility mapping on an intra-urban scale, contributing to studies in risk areas without available data. | pt_PT |
| dc.description.version | info:eu-repo/semantics/acceptedVersion | pt_PT |
| dc.identifier.doi | 10.1007/s10346-022-02001-7 | pt_PT |
| dc.identifier.eissn | 1612-5118 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/19167 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer | pt_PT |
| dc.relation | MCTI/FINEP/FNDCT01/2016 | pt_PT |
| dc.subject | Random forest | pt_PT |
| dc.subject | Landslide susceptibility model | pt_PT |
| dc.subject | DTM | pt_PT |
| dc.subject | LiDAR | pt_PT |
| dc.subject | UAV | pt_PT |
| dc.title | Comparison of LiDAR- and UAV-derived data for landslide susceptibility mapping using Random Forest algorithm | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 600 | pt_PT |
| oaire.citation.issue | 3 | pt_PT |
| oaire.citation.startPage | 579 | pt_PT |
| oaire.citation.title | Landslides | pt_PT |
| oaire.citation.volume | 20 | pt_PT |
| person.familyName | Simoes | |
| person.givenName | Silvio Jorge Coelho | |
| person.identifier | https://scholar.google.com/citations?user=qUrUr0MAAAAJ&hl=en | |
| person.identifier.ciencia-id | DB13-AD27-854F | |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | d1cbe7f3-40ed-4e03-91ca-01446df28812 | |
| relation.isAuthorOfPublication.latestForDiscovery | d1cbe7f3-40ed-4e03-91ca-01446df28812 |