IMPACT OF LEARNING SET AND SAMPLING FOR SNOW AVALANCHE SUSCEPTIBILITY MAPPING WITH RANDOM FOREST

Snow avalanche refers to rapid snow mass movement under the influence of gravity and is a frequently observed natural phenomenon in mountainous regions. An area affected by a snow avalanche consists of starting, track (or transition) and runout (or deposition) zones, which have different geomorpholo...

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Bibliographic Details
Main Authors: Cetinkaya, S., Kocaman, S.
Format: Conference Proceeding
Language:English
Subjects:
Avalanches
Geomorphology
Gravity
Gravity effects
Impact analysis
Learning
Mass movement
Methods
Recall
Snow
Snow avalanches
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Summary:Snow avalanche refers to rapid snow mass movement under the influence of gravity and is a frequently observed natural phenomenon in mountainous regions. An area affected by a snow avalanche consists of starting, track (or transition) and runout (or deposition) zones, which have different geomorphologic characteristics that need to be considered in hazard modelling. This study attempted to analyze the impact of the different zones in producing snow avalanche susceptibility maps (ASMs) with data-driven methods. The random forest (RF) method was applied to the data from Gross Spannort Mountain region (Switzerland) for this purpose. Avalanche inventories from two dates were manually delineated to separate the three zones and two RF models were trained with learning datasets; i.e. Inventory-A which includes all snow avalanche zones (original inventory), and Inventory-B with starting and track zones. The conditioning factors were defined based on the literature, data availability and study area characteristics. The trained models (Model-A with Inventory-A and Model-B with Inventory-B) were evaluated with the area under the receiver operating characteristic (ROC), precision, recall and F1 score. The results show that Model-A has AUC of 0.97, precision of 0.89, recall of 0.95 and F1 score of 0.92 and the Model-B has AUC of 0.98, precision of 0.90, recall of 0.94 and F1 score of 0.92 that indicate high prediction performances for both cases. Furthermore, feature importance values were calculated by the Mean Decrease in Impurity (MDI) method, and elevation, aspect and valley depth were found the most influencing conditioning factors.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLVIII-M-1-2023-57-2023