Assessment of rainfall-induced landslide susceptibility in Artvin, Turkey using machine learning techniques
Yükleniyor...
Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, the performances of machine learning models, such as artificial neural networks (ANN), gradient-boosting machines (GBM), random forest (RF) and support vector machines (SVM) in rainfall-induced landslide susceptibility mapping were evaluated. For this purpose, the Arhavi, Hopa and Kemalpaşa districts of Artvin, which is one of the highest rainfall areas in Turkey, were identified as the study area. A landslide inventory comprising 533 landslide polygons (3959 pixels at 10-m resolution) was used; 70% of the pixels showing the landslides were used for training the models and the remaining 30% were used to validate the models. For landslide susceptibility modelling, 13 factors associated with landslides were considered. The area under the receiver operating characteristic curve was found to reveal the predictive capabilities of the models. As a result, the prediction rates of the ANN, SVM, RF and GBM models were found to be 93.8%, 94.8%, 96.1%, and 97%, respectively. According to the results, the GBM outperformed other models.
Açıklama
Anahtar Kelimeler
Landslide susceptibility, ANN, SVM, RF, GBM
Kaynak
Journal of African Earth Sciences
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
191
Sayı
Künye
Akıncı, H. (2022). Assessment of rainfall-induced landslide susceptibility in Artvin, Turkey using machine learning techniques. Journal of African Earth Sciences, 192, 104535.
