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Background: Forest roads are among the most basic infrastructure used for forestry activities and
services. To facilitate the increased amount of biomass harvesting adequately, the existing road network
may require modifications to allow forest transportation within harvesting units that are not yet accessed
by the roads. The construction of a forest road can trigger landslides, so the necessary constraints should
be considered when the road is being planned to preclude such problems. Landslide Susceptibility
Mapping (LSM) has become an integral part of the growing process of machine learning (ML), providing
a more effective platform for practitioners, planners, and decision-makers. This study aims to reveal the
most suitable alternative routes for a forest road, especially in areas susceptible to landslides, and to
provide an effective tool for decision-makers.
Results: For this purpose, two models were developed through ML: Logistic Regression (LR) and
Random Forest (RF). Elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), Stream Power
Index (SPI), distance from the fault, the road, and the stream, and lithology were considered as the main
landslide susceptibility factors in these models. The best model was obtained by the RF approach with
an Area Under ROC Curve (AUC) value of 81.9%, while the LR model was 78.2%. LSM data was used as
a base, and alternative routes were obtained through CostPath analysis.
Conclusion: It has been shown that the ML methods used in this study can positively contribute to
decision-making by providing more effective LSM calculations in studies to determine alternative routes
in a forest road network.
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