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Articles | Volume XLIII-B3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1083–1090, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1083-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1083–1090, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1083-2022
 
30 May 2022
30 May 2022

SNOW AVALANCHE SUSCEPTIBILITY MAPPING FOR DAVOS, SWITZERLAND

S. Cetinkaya1,2 and S. Kocaman2,3 S. Cetinkaya and S. Kocaman
  • 1Hacettepe University, Graduate School of Science and Engineering, Ankara, Turkey
  • 2Dept. of Geomatics Engineering, Hacettepe University, 06800 Beytepe Ankara, Turkey
  • 3ETH Zurich, Institute of Geodesy and Photogrammetry, 8093 Zurich, Switzerland

Keywords: snow avalanche susceptibility, logistic regression, random forest, remote sensing, machine learning

Abstract. 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.