Evaluation of forest road network planning in landslide sensitive areas by GIS-based multi-criteria decision making approaches in Ihsangazi watershed, Northern Turkey Procjena planiranja mreža šumskih putova u osjetljivim klizištima pomoću GIS baziranih multikriterijskih pristupa odlučivanju na Ihsangazi vododjelnici, Sjeverna Turska

Bugday, Ender; Akay, Abdullah Emin · 2019 · Crossref

DOI: 10.31298/sl.143.7-8.4

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Summary

This study addresses the challenge of planning forest road networks in landslide-sensitive mountainous areas, specifically within the İhsangazi Watershed in Northern Turkey. Forest roads are critical infrastructure for forestry services, yet their construction in steep, unstable terrain increases costs and environmental risks. The research aims to evaluate the effectiveness of different Landslide Susceptibility Mapping (LSM) models to support rational decision-making in road network planning. By identifying high-risk zones, planners can avoid triggering landslides and optimize route selection. The researchers developed and compared 12 distinct LSM models using three Multi-Criteria Decision Making (MCDM) approaches: Modified Analytical Hierarchy Process (M-AHP), Fuzzy Inference System (FIS), and Logistic Regression (LR). These models were generated using Geographic Information System (GIS) software (NetCAD GIS 7.6 and ArcGIS 10.3) and incorporated nine specific factors: elevation, slope, aspect, lithology, distance to faults, distance to streams, distance to roads, Topographic Wetness Index (TWI), and Stream Power Index (SPI). Each approach utilized different combinations of these factors, with M-AHP relying on expert-derived scoring, FIS using membership functions for complex problem solving, and LR employing statistical regression. Model performance was validated using Receiver Operating Characteristic (ROC) analysis and Area Under the Curve (AUC) values, based on historical landslide data from the General Directorate of Mineral Research and Explorations. The results indicated that Logistic Regression models outperformed the other methods. The best-performing model was LR-Model 3, achieving an AUC of 76.6%, followed by LR-Model 4 (AUC = 75.7%) and FIS-Model 4 (AUC = 73.4%). The most successful M-AHP model achieved an AUC of 71.0%. Overall, model success rates ranged from 64.6% to 76.6%. The analysis revealed that the southern part of the watershed is highly susceptible to landslides due to proximity to fault lines and specific topographic conditions. Additionally, the study found that the current forest road density in the southern area is 14.6 m/ha, which is significantly below the target density of 25 m/ha required for effective forest management. The study concludes that integrating GIS-based MCDM approaches, particularly Logistic Regression, provides a robust framework for evaluating forest road planning alternatives in landslide-prone regions. The findings imply that future road construction in the İhsangazi Watershed must be meticulously planned to minimize excavation and avoid triggering landslides, especially in the high-risk southern zone. While road density needs to increase to meet forestry management goals, this expansion must be balanced with stabilization works and careful route selection to ensure environmental integrity and safety. The research demonstrates that these modeling techniques offer a practical advantage for making accurate, rational decisions in forest infrastructure development.

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