Landslide Susceptibility Mapping Using a Random Forest Model on Google Earth Engine in Kuningan Regency
DOI:
https://doi.org/10.23960/jpg.v14.i2.337Keywords:
land susceptibility, random forest, landslide inventory, controlling factor remote sensingAbstract
This study demonstrates that the Random Forest algorithm is effective in mapping landslide susceptibility in the Hantara area and its surrounding regions, as evidenced by strong model performance with an AUC value of 0.868, indicating a high capability to distinguish between landslide and non-landslide locations. Model evaluation shows an AUC value of 0.868, which reflects a strong discriminative performance in separating landslide locations from non-landslide areas. Analysis of variable importance highlights vegetation condition represented by OSAVI, soil physical characteristics indicated by clay content, and topographic parameters, particularly slope class and elevation, as the dominant factors influencing landslide susceptibility. In contrast, rainfall, geological conditions, and land use/land cover contribute as secondary controlling factors. The generated susceptibility probability values range from 0.193 to 0.787 and were subsequently classified into three susceptibility levels. The resulting map is predominantly characterized by the moderate susceptibility class, accounting for 50.46% of the study area, followed by the high (26.60%) and low (22.94%) susceptibility classes. These findings demonstrate that the integration of remote sensing–derived variables with the Random Forest approach provides a robust spatial analysis framework for supporting landslide risk mitigation and management in the study area.
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