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ŠUMARSKI LIST 3-4/2022 str. 54     <-- 54 -->        PDF

modeling methods, which are widely used in machine learning (ML). Two models with the highest receiver operating characteristic (ROC) and area under curve (AUC) values were selected, and ten factors (slope, elevation, lithology, distance to road, distance to fault, distance to river, curvature, stream power index, topographic position index, and topographic wetness index) were used. The best LSM modeling method was AUC. The AUC value was 90.6% with the RF approach and 80.3% with the LR approach. The generated LSMs were used to determine alternative routes that were calculated through cost path analysis. It is hoped that the susceptibility to landslides and selection of alternative forest road routes determined through the approaches and techniques in this study will benefit forest road planning as well as plan and decision makers.
Key words: forestry, alternative route detection, cost-path, random forest, logistic regression