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oa Data-Driven Landslide Susceptibility Analysis in the Ukrainian Carpathians: A Machine Learning Approach
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 5th EAGE Workshop on assessment of landslide hazards and impact on communities, Sep 2025, Volume 2025, p.1 - 5
Abstract
The Carpathian region of Ukraine is notably prone to landslides because of its complex geology, rugged terrain, and relatively high rainfall. This investigation presents the first implementation of a machine learning (ML) method—specifically, the eXtreme Gradient Boosting (XGBoost) algorithm—for landslide susceptibility mapping in the Transcarpathian region of Ukraine. Utilizing a landslide inventory comprising 697 recorded landslides, the model integrated ten predisposing factors selected through Variance Inflation Factor (VIF) analysis to mitigate multicollinearity. The model’s efficacy was measured using the Receiver Operating Characteristic (ROC) curve, which yielded an Area Under the Curve (AUC) score of 0.71. Although the model did not demonstrate high predictive performance, this first-of-its-kind ML application marks a critical step towards improved geohazard management in Ukraine.