A comprehensive analysis of landslide susceptibility in Iyidere Basin (NE, Turkey) using machine learning techniques and statistical bivariate methods
Yazarlar (2)
Dr. Öğr. Üyesi Kemal ERSAYIN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Ali Uzun Ondokuz Mayıs Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Natural Hazards (Q1) | ||
| Dergi ISSN | 0921-030X Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 05-2025 |
| Cilt / Sayı / Sayfa | 121 / 12 / 14283–14319 | DOI | 10.1007/s11069-025-07354-5 |
| Makale Linki | https://doi.org/10.1007/s11069-025-07354-5 | ||
| Özet |
| Natural events are called disasters when they cause great damage, human suffering, or loss of life. Landslides, one of these disasters, cause significant damage to property and infrastructure and pose risks to people's lives. In this research, landslide susceptibility was studied in Iyidere Basin, located in northeastern Turkey. This basin, which is among the cities where the most landslide events occur in Turkey, is a very important representative area in terms of a comprehensive analysis of landslides in the region. Bivariate (frequency ratio, weight of evidence, statistical index) and machine learning methods (artificial neural network, logistic regression) were used to evaluate landslide susceptibility with fifteen environmental parameters and 588 landslide inventory data. Landslide inventory data was generated using different sources, and environmental parameters databases were created using various sources and … |
| Anahtar Kelimeler |
| Bivariate statistical methods | Landslide susceptibility | Machine learning | Rize | İyidere |
| Atıf Sayıları | |
| Google Scholar | 6 |
| Scopus | 5 |
| Web of Science | 5 |