The influence of sampling strategies on shallow landslide susceptibility modelling in a mountainous area
Yazarlar (1)
Dr. Öğr. Üyesi Kemal ERSAYIN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Landslides (Q1) | ||
| Dergi ISSN | 1612-510X Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2026 |
| Cilt / Sayı / Sayfa | 23 / 2 / 531–546 | DOI | 10.1007/s10346-025-02646-0 |
| Makale Linki | https://doi.org/10.1007/s10346-025-02646-0 | ||
| UAK Araştırma Alanları |
Coğrafi Bilgi Sistemleri
|
||
| Özet |
| Susceptibility modeling is valuable in combating landslides, one of the natural disasters that cause significant adverse impacts worldwide. The most important data for modeling landslide susceptible areas are inventories containing records of past landslides. How to sample these inventory data and include them as dependent variables in the susceptibility model is controversial. In this study, we aimed to evaluate the effects of different sampling strategies on landslide susceptibility models. Eight different grid-based sampling strategies were presented in assessing shallow landslides in a mountainous area. We modeled landslide susceptibility using these sampling strategies. Extreme gradient boosting, a machine learning method, and ten factors (elevation, slope, plan curvature, profile curvature, topographic position index, topographic wetness index, stream power index, topographic roughness index, distance to … |
| Anahtar Kelimeler |
| Extreme gradient boosting | Landslide presence sampling | Landslide sampling | Landslide susceptibility | Machine learning | Sampling strategies |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Scopus | 2 |
| Google Scholar | 2 |