Landslide Susceptibility Assessment by Using Convolutional Neural Network

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Nikoobakht S., Azarafza M., AKGÜN H., Derakhshani R.

Applied Sciences (Switzerland), cilt.12, sa.12, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 12
  • Basım Tarihi: 2022
  • Doi Numarası: 10.3390/app12125992
  • Dergi Adı: Applied Sciences (Switzerland)
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: artificial intelligence, convolutional neural networks (CNN), deep-learning, susceptibility assessment, Gorzineh-khil region, BLACK-SEA REGION, LOGISTIC-REGRESSION, MULTICRITERIA DECISION, AREA, COUNTY, BIVARIATE, MACHINE, MODELS, TURKEY, RATIO
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

© 2022 by the authors. Licensee MDPI, Basel, Switzerland.This study performs a GIS-based landslide susceptibility assessment using a convolutional neural network, CNN, in a study area of the Gorzineh-khil region, northeastern Iran. For this assessment, a 15-layered CNN was programmed in the Python high-level language for susceptibility mapping. In this regard, as far as the landside triggering factors are concerned, it was concluded that the geomorphologic/topographic parameters (i.e., slope curvature, topographical elevation, slope aspect, and weathering) and water condition parameters (hydrological gradient, drainage pattern, and flow gradient) are the main triggering factors. These factors provided the landside dataset, which was input to the CNN. We used 80% of the dataset for training and the remaining 20% for testing to prepare the landslide susceptibility map of the study area. In order to cross-validate the resulting map, a loss function, and common classifiers were considered: support vector machines, SVM, k-nearest neighbor, k-NN, and decision tree, DT. An evaluation of the results of the susceptibility assessment revealed that the CNN led the other classes in terms of 79.0% accuracy, 73.0% precision, 75.0% recall, and 77.0% f1-score, and, hence, provided better accuracy and the least computational error when compared to the other models.