Generating Landslide Susceptibility Maps Using Mathematical Models and UAV data: The Case of Çankırı Region in Türkiye

Authors : Abdullah ÖZÇELİK, Ender BUĞDAY

Pages : 1-10

Doi:10.33904/ejfe.1066040

View : 15 | Download : 8

Publication Date : 2022-06-30

Article Type : Research Paper

Abstract :Landslides are natural disasters that affect not only residential areas but alos forest ecosystems. In order to determine the areas with high landslide risk and take necessary measures in risky areas, landslides susceptible should analyzed and susceptible map insert ignore into journalissuearticles values(LSM); should be developed in advance. In this study, a LSM was produced for two study areas with different sizes including Çankırı province and in the Ilısılık Village of Çankırı in Türkiye. Analytical Hierarchy Process insert ignore into journalissuearticles values(AHP); and Logistic Regression Modeling insert ignore into journalissuearticles values(LRM); methods were used to generate LSM based on the main factors including elevation, slope, lithology, distance to faults - streams and roads. For Çankırı province, 30 m resolution Digital Elevation Model insert ignore into journalissuearticles values(DEM); was used to produce the map while one-meter resolution Digital Terrain Model insert ignore into journalissuearticles values(DTM);, generated by using Unmanned Aerial Vehicle insert ignore into journalissuearticles values(UAV);, was used for Ilısılık Village. As a result of the study, AHP model success was calculated as 73.9% and 91.7% for Çankırı and Ilısılık, respectively, considering the previous landslides occurred in the region. On the other hand, LRM model success was 75.2% and 93.1%, respectively. It was also indicated that DTM data is advantageous to DEM data by offering a more precise and detailed usage opportunity. The sensitivity is revealed more clearly and effectively in precision planning studies such as risk mapping of natural disasters that requires special measurement in small areas.
Keywords : Natural disaster, Landslide, unmanned aerial vehicle, susceptibility mapping, modelling