Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria

Researchers developed multi-hazard exposure maps for flooding and landslides in the State of Salzburg, Austria, using machine learning techniques—specifically support vector machines (SVM) and random forest (RF). The study identified 13 key influencing factors, including elevation, slope, rainfall, and land cover. Random Forest outperformed SVM in accuracy, achieving an R-Index of 0.90 for landslides and 0.87 for floods, compared to SVM's 0.89 and 0.87 respectively. These maps provide critical tools for urban planners and risk managers to better identify and mitigate high-risk regions affected by both hazards.

Nachappa, T., Ghorbanzadeh, O., Gholamnia, K., & Blaschke, T. (2020). Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria. Remote Sensing, 12(17), 2757. https://doi.org/10.3390/rs12172757