DIGITALNA ARHIVA ŠUMARSKOG LISTA
prilagođeno pretraživanje po punom tekstu
|ŠUMARSKI LIST 3-4/2022 str. 45 <-- 45 --> PDF|
platform for planners and decision makers to identify alternative routes by using modern methods. Sixteen different models were created through two different ML approaches: LR and RF modeling. Alternative forest roads were computed using the same approaches. Curvature, distance to fault, lithology, distance to road, slope (degree), stream power index (SPI), distance to stream, topographic position index (TPI), and topographic wetness index (TWI) factors were used for the modeling. The two models with the highest AUC value were used to calculate alternative routes by using least cost path analysis and ArcGIS software. During the last phase of this study, the alternative routes were compared with a route created through the classical approach.
MATERIAL AND METHODS
MATERIJAL I METODE
Study Area – Prostorno područje
The study area was conducted in the Erikli region, Yapraklı District, of the Çankırı province in northern Turkey. The area falls under the administrative responsibility of the Erikli Forestry Operations Directorate, which is affiliated to the Çankırı Forestry Operations Directorate of Ankara Regional Directorate of Forestry. This area has a history of landslide events. The protection forests have an area of 149.38 km2 and are located at a latitude between 40° 37’ 47’’ and 40° 44’ 46’’ and a longitude between 33° 56’ 34’’ and 34° 05’ 25’’. Black pine (Pinus nigra Arnold) forests dominate this area while there are also stands. These forests have been named as protection forests by the General Directorate of Forestry (GDF). Almost all roads in the studied area are forest roads with road formation width of 6 m, and their total length is 157.96 km. The general forest road density is 10.57 m/ha. The area’s altitude varies between approximately 790 m and 1,520 m with an average elevation of 1,210 m. The maximum slope is 67.6 degrees, and the average is 16.5 degrees. During the training phase, data on 70% (61 landslides) of the total 87 landslides that occurred in the studied area were selected, and data on the remaining 30% (26 landslides) were used during the testing phase.
LSM Modeling Process – LSM Proces modeliranja
The digital elevation model (DEM) was obtained free of charge from ASTER-GDEM (published on the web) and elevation, curvature, slope, SPI, TPI, and TWI factors were created using ArcGIS 10.3 TM software. Distance to road was obtained from the Forest Subdistrict databases. Lithology, distance to fault, and distance to stream factors, as well as field data of past landslides, were obtained from the General Directorate of Mineral Research and Explorations (GDMRE) (Duman et al. 2011).
Curvature (Figure 2a) is commonly used in LSM modeling studies because it helps predict the direction and severity of landslides (Dou et al. 2019). Distance to fault is an important and significant factor in the triggering of landslides (Massey et al. 2020). In the studied area, the distances were 0.5, 1, 3, 5, 10, and 20 km (Figure 2b). The lithology factor has high significance; it determines both landslide susceptibility and cost of forest road construction since it provides information on the characteristics of the bedrock (Boroughani et al. 2020). For this study, lithology was evaluated for five different groups (Figure 2c). Distance to road is an artificial factor affecting landslide formation that is frequently used in national and international literature to determine landslide susceptibility (Li and Chen 2020; Bugday and Akay 2019) (Figure 2d). Slope is a main factor in landslide formation (Pourghasemi et al. 2021) and, similar to the lithology factor, also affects cost. Five different classes (0°–5.71⁰, 5.71⁰–13.80⁰, 13.80⁰–21.88⁰, 21.88⁰–31.99⁰ and > 32⁰) were added to the analysis based on the International Union of Forest Research Organizations (IUFRO) slope classes (commonly used in Turkey) (Figure 2e). The stream power index (SPI) was computed with the assumption that the flow (q) is proportional to the specific catchment area (As) and is expressed as the ability of the current water flow in the basin to cause erosion (Achour and Pourghasemi 2020) (Figure 2f). Distance to stream is a factor commonly employed in studies that consider the significance of proximity relations in landslide susceptibility (Senouci et al. 2021). Distances are expressed as zones with 0.5, 1, 2, 5 and 10 km intervals (Figure 2g). Topographic Position Index (TPI) is used in landslide susceptibility studies to determine the cell position relative to ridges and valleys, where positive values represent ridges, negative values represent valleys, and zero values represent flat areas (Jenness 2006) (Figure 2h). The TWI factor is used to express the location and spatial dimensions of water-saturated areas (Eiras et al., 2021) (Figure 2i).
LSM Process – LSM proces
In this study, the LSM Tool Pack, developed by Sahin et al. (2020), was used to create an LSM. LSM prediction models were created using the factors shown in Figure 2, using LR and RF modeling methods.
ArcGIS 10.3 software used LR and RF methods to evaluate the factors for this study. Information on past landslide events and on areas where landslides have never occurred was tested to validate the models. The validation of the models created by LR and RF methods was tested using receiver operating characteristic (ROC) analysis and the Area Under ROC Curve (AUC) value. In the literature, the AUC score is expressed as follows: 0.9–1.0 = excellent; 0.8–0.9 = very good; 0.7–0.8 = good; 0.6–0.7 = moderate; 0.5–0.6 = poor (Bradley, 1997).