Random Forest-Based Landslide Susceptibility Mapping in Coastal Regions of Artvin, Turkey

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AKINCI H., Kilicoglu C., Doğan S.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, cilt.9, sa.9, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9 Sayı: 9
  • Basım Tarihi: 2020
  • Doi Numarası: 10.3390/ijgi9090553
  • Dergi Adı: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, CAB Abstracts, INSPEC, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: landslides, landslide susceptibility, machine learning, random forest, Artvin, SPATIAL PREDICTION MODELS, SUPPORT VECTOR MACHINE, 3 GORGES RESERVOIR, LOGISTIC-REGRESSION, FREQUENCY RATIO, INFORMATION VALUE, DECISION TREE, NEURAL-NETWORK, OF-EVIDENCE, GIS
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

Natural disasters such as landslides often occur in the Eastern Black Sea region of Turkey owing to its geological, topographical, and climatic characteristics. Landslide events occur nearly every year in the Arhavi, Hopa, and Kemalpasa districts located on the Black Sea coast in the Artvin province. In this study, the landslide susceptibility map of the Arhavi, Hopa, and Kemalpasa districts was produced using the random forest (RF) model, which is widely used in the literature and yields more accurate results compared with other machine learning techniques. A total of 10 landslide-conditioning factors were considered for the susceptibility analysis, i.e., lithology, land cover, slope, aspect, elevation, curvature, topographic wetness index, and distances from faults, drainage networks, and roads. Furthermore, 70% of the landslides on the landslide inventory map were used for training, and the remaining 30% were used for validation. The RF-based model was validated using the area under the receiver operating characteristic (ROC) curve. Evaluation results indicated that the success and prediction rates of the model were 98.3% and 97.7%, respectively. Moreover, it was determined that incorrect land-use decisions, such as transforming forest areas into tea and hazelnut cultivation areas, induce the occurrence of landslides.