Assessing and mapping landslide susceptibility using different machine learning methods
| dc.authorid | 0000-0002-1362-8206 | |
| dc.authorid | 0000-0002-0881-0396 | |
| dc.authorid | 0000-0002-7594-5522 | |
| dc.authorid | 0000-0001-9335-1747 | |
| dc.authorid | 0000-0002-8629-1077 | |
| dc.contributor.author | Orhan, Osman | |
| dc.contributor.author | Bilgilioğlu, Süleyman Sefa | |
| dc.contributor.author | Kaya, Zehra | |
| dc.contributor.author | Özcan, Adem Kürşat | |
| dc.contributor.author | Bilgilioğlu, Hacer | |
| dc.date.accessioned | 2022-09-07T11:09:11Z | |
| dc.date.available | 2022-09-07T11:09:11Z | |
| dc.date.issued | 2022 | |
| dc.department | Mühendislik Fakültesi | |
| dc.description.abstract | The main aim of the present study was to produce and compare landslide susceptibility maps by using five machine learning techniques, namely, artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), random forest (RF) and, classification and regression tree (CART). The study area was determined as the Arhavi-Kabisre river basin, a region in which the most landslide incidents occur in Turkey. Firstly, a landslide inventory was produced by identifying a total of 252 landslides. Secondly, a total of 11 landslide conditioning factors were considered for the landslide susceptibility mapping. Subsequently, the five machine learning techniques were constructed with the help of the training dataset for the landslide susceptibility maps. Finally, the receiver operating characteristic (ROC), sensitivity, specificity, F-measure, accuracy and kappa index were applied to compare and validate the performance of the five machine learning techniques. | |
| dc.identifier.doi | 10.1080/10106049.2020.1837258 | |
| dc.identifier.endpage | 2820 | en_US |
| dc.identifier.issn | 1010-6049 | |
| dc.identifier.issn | 1752-0762 | |
| dc.identifier.issue | 10 | en_US |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | 2795 | en_US |
| dc.identifier.uri | https:/dx.doi.org/10.1080/10106049.2020.1837258 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/9675 | |
| dc.identifier.volume | 37 | en_US |
| dc.identifier.wos | WOS:000582873700001 | |
| dc.identifier.wosquality | Q1 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Taylor & Francis | |
| dc.relation.ispartof | Geocarto International | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/embargoedAccess | |
| dc.subject | Artificial Neural Network | |
| dc.subject | Artvin | |
| dc.subject | Landslidesupport Vector Machine | |
| dc.subject | Susceptibility | |
| dc.title | Assessing and mapping landslide susceptibility using different machine learning methods | |
| dc.type | Article |
