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Cetinkaya S., Kocaman S.

24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Nice, France, 6 - 11 June 2022, vol.43-B3, pp.1083-1090 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 43-B3
  • Doi Number: 10.5194/isprs-archives-xliii-b3-2022-1083-2022
  • City: Nice
  • Country: France
  • Page Numbers: pp.1083-1090
  • Keywords: Snow avalanche susceptibility, logistic regression, random forest, remote sensing, machine learning, MOUNTAINS
  • Hacettepe University Affiliated: Yes


Snow avalanches are among destructive hazards occurring in mountainous regions and spatial distribution (susceptibility) of their occurrences needs to be considered for spatial planning and disaster risk mitigation efforts. The susceptibility assessment is the first step in avalanche disaster management and can be carried out using high resolution geospatial data and machine learning (ML) algorithms. In this study, we have assessed the snow avalanche susceptibility in Davos, Switzerland using an inventory delineated on satellite imagery in a previous study. The conditioning factors used for the avalanche susceptibility assessment include elevation, slope, plan curvature, profile curvature, aspect, topographic position index, topographic ruggedness index, topographic wetness index, land use and land cover, lithology, distance to road, and distance to the river. Two ML algorithms, the logistic regression (LR) and the random forest (RF), were comparatively assessed using validation data split from the training data (30/70). The prediction performances of both models were assessed based on the area under the receiver operating characteristic curve (ROC-AUC) value. Although the AUC value obtained from the LR method was relatively low (0.74), the value obtained from the RF (0.96) demonstrated high performance and usability of this approach. The results indicate that the RF method can successfully produce an avalanche susceptibility map for the region, although potential improvements may be possible by investigating various input features and ML algorithms as well as by classifying the starting and runout zones of the avalanche data separately. Furthermore, the accuracy is expected to increase by using a larger training dataset.