Landslide Susceptibility Mapping Using Weight of Evidence Method in Tieng Village and Surrounding Areas, Kejajar District, Wonosobo Regency, Central Java Province, Indonesia - Fenora Aninditya Pramesthi; Wahyu Wilopo; Agus Hendratno; Hendy Setiawan; I Gde Budi indrawan; Doni Prakasa Eka putra; Wahyu Sasongko (Journal of Applied Geology, 2025)
Tieng village and its surrounding areas in Kejajar District, Wonosobo Regency, are identified as regions with a relatively high risk of landslide hazards. This research aims to examine the geological conditions influencing landslide occurrences and to develop a landslide susceptibility zonation map within the study area. Methods of landslide mapping using statistical method weight of evidence (WoE), with parameters including slope, lithology and grade of weathering, distance from lineaments, distance from rivers, and land use. All parameters were classified into several class and spatially processed using ArcGIS software. Then, these maps were overlaid and analyzed based on contrast weight values to produce the final landslide susceptibility zonation map. The resulting susceptibility map categorized into four zones: very low, low, medium, and high landslide susceptibility. The very low landslide susceptibility zone is generally found in the eastern part of the area, the low susceptibility zone is in the western and eastern parts, the medium susceptibility zone occurs in the eastern and southern parts, while the high susceptibility zone is distributed in the southeastern, central, and eastern parts of the study area. The parameters that have the most influence on landslide susceptibility in the research area based on their level of influence from strongest to weakest are land use, distance from lineament, distance from river, lithology and grade of weathering and slope. The accuracy of the landslide susceptibility map was assessed using the Success Rate Curve (SRC) and Prediction Rate Curve (PRC) validation. The Area Under Curve (AUC) values obtained from the SRC and PRC analyses were 0.765 for SRC and 0.741 for PRC, respectively, indicating a good level of model performance.