A GIS BASED LANDSLIDE SUSCEPTIBILITY MAPPING USING MACHINE LEARNING AND ALTERNATIVE FOREST ROAD ROUTES ASSESSMENT IN PROTECTION FORESTS

Forestry activities should be carried out within the purview of sustainable forestry while reaping the benefits of forestry. Accordingly, the construction of forest roads through forests should be carefully planned, especially in protection forests. Forest areas in Turkey are generally widespread in mountainous and high sloping areas that are susceptible to landslides-landslide susceptibility is one of the most important criteria for the selection of protected forests. As such, it is important to evaluate detailed and applicable alternatives regarding special areas and private forests. The aim of this study is to determine alternative routes for forest roads in protected forests through the use of geographic information systems (GIS), particularly in areas with high landslide susceptibility. To this end, a landslide susceptibility map (LSM) was created using logistic regression (LR) and random forest (RF) 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.