Assessment of Forest Road Networks for Landslide Susceptibility: A Case Study of Northern Forest Area in Türkiye

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dc.contributor.authorGenc, Cigdem Ozer
dc.contributor.authorAkinci, Halil
dc.contributor.authorKilicoglu, Cem
dc.contributor.authorAricak, Burak
dc.contributor.authorDogan, Sedat
dc.date.accessioned2026-02-08T15:16:04Z
dc.date.available2026-02-08T15:16:04Z
dc.date.issued2026
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractLandslides, which usually occur in mountainous and hilly areas, occur as a result of the soil or rock material forming a slope moving down under the influence of gravity. Forested areas, mostly in mountainous regions, are susceptible to landslides. Forest roads are important infra-structure facilities to protect forest resources and to achieve sustainable management objectives. Forest roads provide many benefits such as facilitating the transportation of wood raw materials, preventing fires and providing access to areas where recreational activities are carried out. How-ever, inappropriately opened forest roads in forest areas cause problems such as landslides, which cause both serious destruction of road networks and serious deformations in forest areas. Land-slide-prone forest roads also cause serious economic losses due to disruption of product transport and road maintenance costs. Within the scope of this study, landslide susceptibility maps (LSMs) were produced to determine the relationship between landslides and landslide-causing factors in Hand & uuml;z & uuml; Forest Management Unit of Kastamonu Regional Directorate of Forestry (KRDF) located in the Central Black Sea Region of T & uuml;rkiye. Land use, altitude, slope, aspect, plan and profile curvature, topographic wetness index (TWI), distance to forest road, drainage networks and fault, crown closure and lithology were used as conditioning factors in the study. Logistic Regression (LR) and Support Vector Machine (SVM) based machine learning models were used to generate LSMs. The receiver operating characteristics (ROC) curve and area under the ROC curve (AUC) method were used to compare the performance of landslide susceptibility models. In the accuracy assessment using the prediction rate curve, the AUC value was 0.968 for the SVM model and 0.668 for the LR model. The AUC values confirmed that SVM performed much better than LR. In addition, the susceptibility of newly planned forest roads (not currently avail-able in the field) in LSMs were determined in the study. As aresult of the study, it was determined that the most effective factors affecting landslides in Hand & uuml;z & uuml; Forest Management Directorate are distance to forest roads and drainage networks. In the analyses, it was found that 28.28% of the existing forest roads in the LSM produced with SVM and 56.57% in the LSM produced with LR were found to be in >> high<< and >> very high<< landslide susceptible areas. Similarly, 38.43% of the newly planned roads in the LSM produced with SVM and 52.23% in the LSM produced with LR were found to be in >> high<< and >> very high<< landslide susceptible areas. These findings showed that forest roads are the main factor in the occurrence of landslides in the study area. Therefore, taking LSMs into account in the planning of forest roads will contribute to re-ducing the damages that may occur in forest areas due to landslides.
dc.identifier.doi10.5552/crojfe.2026.3434
dc.identifier.endpage202
dc.identifier.issn1845-5719
dc.identifier.issue1
dc.identifier.startpage185
dc.identifier.urihttps://doi.org/10.5552/crojfe.2026.3434
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6121
dc.identifier.volume47
dc.identifier.wosWOS:001651505200013
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherZagreb Univ, Fac Forestry And Wood Technology
dc.relation.ispartofCroatian Journal of Forest Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectlandslide susceptibility mapping
dc.subjectHand & uuml;z & uuml
dc.subjectforest management unit
dc.subjectLogistic Regression (LR)
dc.subjectsupport vector machine (SVM)
dc.subjectdistance to forest roads
dc.subjectKastamonu
dc.titleAssessment of Forest Road Networks for Landslide Susceptibility: A Case Study of Northern Forest Area in Türkiye
dc.typeArticle

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