Assessment of Landslide Susceptibility of Gümüşhane Using Machine Learning Algorithms

Yıl 2026, Cilt: 7 Sayı: 1, 195 - 211, 26.03.2026

Hasan Tahsin Bostancı

https://doi.org/10.48123/rsgis.1836037

https://izlik.org/JA79MB63YE

Öz

Gümüşhane is one of the provinces in Turkey where mass movements such as landslides, rockfalls, and avalanches frequently occur due to its geological structure, rugged topography, and climatic characteristics. The provincial disaster risk reduction plan prepared in 2021 states that landslides account for 49% of all natural disasters occurring throughout the province. Therefore, this study aims to produce landslide susceptibility maps for the Central District of Gümüşhane using machine learning algorithms. In this study, logistic regression (LR), artificial neural networks (ANN), and support vector machines (SVM) were preferred as machine learning algorithms. Ten factors were used in the susceptibility models created with the aforementioned algorithms: aspect, distance to drainage networks, distance to roads, elevation, land cover, lithology, plan curvature, profile curvature, slope, and topographic wetness index (TWI). The landslide inventory map obtained from the General Directorate of the MTA was used in the training and validation stages of the models. The performance of the LR, ANN, and SVM models was evaluated using the area under the ROC curve (AUC) metric. The AUC values of the LR, ANN, and SVM models were determined to be 0.784, 0.865, and 0.888, respectively. These results indicate that the SVM model performed better than the other models. Additionally, it was determined that the most influential factors in the occurrence of landslides in the study area were slope, elevation, TWI, and distance to roads, in that order.

Anahtar Kelimeler

Landslides , Machine learning , LR , ANN , SVM , Gümüşhane

Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi

https://izlik.org/JA79MB63YE

Öz

Gümüşhane, jeolojik yapısı, engebeli topografyası ve iklimsel özellikleri nedeniyle ülkemizde heyelan, kaya düşmesi ve çığ gibi kütle hareketlerinin sıkça yaşandığı illerden biridir. 2021 yılında hazırlanan il afet risk azaltma planında il genelinde meydana gelen doğal afetlerin %49’unu heyelanların oluşturduğu belirtilmiştir. Dolayısıyla bu çalışmada, makine öğrenmesi algoritmaları kullanılarak Gümüşhane’nin Merkez ilçesinin heyelan duyarlılık haritalarının üretilmesi amaçlanmıştır. Çalışmada makine öğrenmesi algoritmaları olarak lojistik regresyon (LR), yapay sinir ağları (ANN) ve destek vektör makineleri (SVM) tercih edilmiştir. Adı geçen algoritmalarla oluşturulan duyarlılık modellerinde; bakı, drenaj ağlarına uzaklık, yola uzaklık, yükseklik, arazi örtüsü, litoloji, plan eğriliği, profil eğriliği, eğim ve topografik nemlilik indeksi (TWI)’nden oluşan 10 faktör kullanılmıştır. Modellerin eğitim ve doğrulama aşamasında MTA Genel Müdürlüğünden temin edilen heyelan envanter haritası kullanılmıştır. LR, ANN ve SVM modellerinin performansı ROC eğrisi altında kalan alan (area under the ROC curve - AUC) metriği kullanılarak değerlendirilmiştir. LR, ANN ve SVM modellerinin AUC değerleri, sırasıyla, 0.784, 0.865 ve 0.888 olarak belirlenmiştir. Bu sonuçlar SVM modelinin diğer modellerden daha iyi performans gösterdiğini ortaya koymuştur. Ayrıca, çalışma alanında heyelanların meydana gelmesinde en etkili faktörlerin sırasıyla eğim, yükseklik, TWI ve yola yakınlık olduğu belirlenmiştir. 

Anahtar Kelimeler

Heyelan , Makine öğrenmesi , LR , ANN , SVM , Gümüşhane

Kaynakça

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