1887

Abstract

Summary

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.

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/content/papers/10.3997/2214-4609.2025520031
2025-09-15
2026-01-16
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